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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 3,049 Documents
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Hybrid deep learning architecture for autism spectrum disorder detection from gait kinematic data Gulzat Ziyatbekova; Svetlana Beglerova; Dinara Zhukenova; Alokhon Alikarieva; Madi Akhmetzhanov; Nuriddin Alikariev; Quvvatali Rakhimov; Yersultan Tulebayev
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11549

Abstract

Early detection of autism spectrum disorder (ASD) is essential for enabling timely interventions that significantly impact developmental outcomes. Traditional diagnostic procedures often rely on clinical observations and subjective assessments, which can reduce objectivity and delay diagnosis. This study presents an automated classification approach for detecting ASD in children using gait kinematic data obtained through video-based skeletal tracking. We propose a hybrid deep learning architecture that combines one-dimensional convolutional layers (Conv1D), bidirectional long short-term memory (BiLSTM) networks, and a multi-head attention mechanism to capture complex spatiotemporal patterns in motion data. The dataset includes 100 participants—50 diagnosed with ASD and 50 typically developing (TD) peers. Preprocessing steps included Euclidean norm transformation, logarithmic scaling, Z-score normalization, and sliding window segmentation. The proposed model achieved 94.9% accuracy, 0.91 recall, 0.86 precision, and an area under the curve (AUC) of 0.97, outperforming a baseline long short-term memory (LSTM) architecture. These findings demonstrate the potential of gait-based kinematic features and hybrid neural networks for objective and reproducible ASD screening. The developed system can contribute to AI-assisted decision support tools in clinical and educational environments, enhancing diagnostic accuracy, and supporting inclusive developmental care.
Performance analysis of adaptive channel coding with OFDM modulation over Rayleigh fading channels Sangeetha, Venkatesan; Sudha, Pattipati Naga
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10807

Abstract

In the contemporary wireless communication system, the orthogonal frequency division multiplexing (OFDM) in conjunction with the use of the error correction code scheme is important towards ensuring a reliable transmission of data through the fading channel. This work is a complete analysis of performance of OFDM modulation combined with adaptive channel coding schemes are low-density parity check (LDPC), turbo, and convolutional code while using the Rayleigh fading channel. Error correction schemes such as LDPC, turbo, and convolutional code are used with a variety of trade-offs on their error correction performance. The system is coded in MATLAB/Simulink and run with 64 subcarriers, a length of cyclic prefix of 16 and a 16-quadrature amplitude modulation (16-QAM) modulation, channel coding techniques which are preferred upon the levels of signal-to-noise ratio (SNR) between 0 to 20 dB and the level of performance is measured by bit error rate (BER). The simulation findings prove that LDPC code achieves the highest performance at high SNR values, convolutional code has moderate BER at mid-SNR range, and turbo code has superior performance at low-SNR range and outperforming both LDPC and convolutional codes. These findings indicate that adaptive channel coding in the OFDM system is effective over Rayleigh fading.
Generating synthetic strike-through handwritten text using generative adversarial networks Raje, Dheemanth Urs; Krishanappa, Chethan Hasigala
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10358

Abstract

The evaluation of handwritten text documents is a significant research field in text analysis. Deep learning classifiers' effectiveness tends to decline when faced with variations in text style and the presence of strike-out text components. These strike-outs can occur at the character, word, paragraph, or page level. When these documents undergo optical character recognition (OCR) processing, they often yield inaccurate results. Data unavailability, imbalance poses a strong risk in this area. Hence usage of synthetic datasets for conducting research can solve the issue to an extent. Despite using traditional data augmentation techniques like rotation, position shifting, zooming, and shearing, the issue of imbalanced class distribution remained unchanged. In order to tackle this challenge this paper demonstrates the creation of a synthetic dataset comprising of strikethrough text using modified version of generative adversarial networks (GANs) that has an auxiliary network for text recognition. IAM and RIMES dataset are used as base for the GAN to generate synthetic images of handwritten text. A line segmentation technique is also implemented to create strike outs on random words selected from the synthetic image set thereby increasing the number of strikes. The work believes that the simulated dataset will significantly improve the quality of handwriting text recognition models.
Ultra-lightweight hybrid authentication for MQTT/MQTT-SN internet of thing security Nabeel Alassaf; Selvakumar Manickam; Ammar Odeh; Mohammed Anbar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11907

Abstract

The rapid growth of internet of thing (IoT) has increased the need for secure communication among resource-constrained devices using lightweight protocols such as message queuing telemetry transport (MQTT) and message queuing telemetry transport for sensor network (MQTT-SN). Traditional certificate-based solutions introduce significant computational and memory overhead for low-power devices. This paper proposes the hybrid lightweight protocol (HLP), a certificate-free approach combining elliptic-curve key exchange, hash-based message authentication code (HMAC)-based authentication, and ChaCha20-Poly1305 encryption. HLP uses pre-shared keys to reduce handshake complexity while maintaining confidentiality, integrity, and mutual authentication across MQTT and MQTT-SN environments. A Python-based implementation using paho-mqtt was evaluated in a constrained-device testbed. Experimental results show that HLP achieves lower handshake latency (-20–24 ms) and reduced bandwidth overhead (-130 bytes) compared with elliptic curve Diffie-Hellman ephemeral-pre-shared key (ECDHE-PSK) and elliptic curve Diffie-Hellman ephemeral-elliptic curve digital signature algorithm (ECDHE-ECDSA), while still supporting forward secrecy. These findings demonstrate that HLP is an efficient and practical solution for securing IoT communications on constrained devices.
Integrating hybrid deep learning and CSI for multi-interval hydrological data in enhanced flood prediction Indrastanti Ratna Widiasari; Eko Sediyono; Rissal Efendi
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11278

Abstract

Flood prediction accuracy is often constrained by heterogeneous and asynchronous hydrological data collected at different time intervals. This study proposes a hybrid deep learning–based flood prediction framework that integrates long short-term memory (LSTM), convolutional neural network (CNN), and cubic spline interpolation (CSI) to address these challenges. Rainfall, river discharge, and water level data representing upstream, midstream, and downstream conditions of the Bengawan Solo watershed were utilized. CSI was applied as a preprocessing step to harmonize multi-interval data, reduce noise, and recover missing observations, thereby improving data consistency. The experimental results show that the proposed hybrid LSTM–CNN model enhanced with CSI outperforms baseline LSTM and non-interpolated hybrid models, achieving a mean absolute percentage error (MAPE) of 5.84%, root mean square error (RMSE) of 0.125 m, mean absolute error (MAE) of 0.082 m, and R² of 0.948. The integration of spatio-temporal feature learning with data harmonization enables more accurate flood level prediction and supports timely flood early warning systems. The proposed approach demonstrates strong potential for improving flood risk management and disaster preparedness in flood-prone regions.
An innovative approach to identifying triangular arbitrage opportunities in financial markets using the Bellman-Ford algorithm Akouaouch, Issam; Bouayad, Anas
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10817

Abstract

Existing arbitrage detection techniques rely on exhaustive search or linear programming, which are computationally expensive and often miss profitable cycles in dynamic markets. Triangular arbitrage is a profitable trading strategy that exploits discrepancies in currency exchange rates, but common algorithms detect only a limited number of loops and cannot find non-loop opportunities. To address these gaps, this study presents a realtime, graph-based framework for identifying triangular arbitrage opportunities in cryptocurrency markets using an optimized implementation of the Bellman–Ford algorithm. By modeling currency exchange rates as a directed graph and detecting negative-weight cycles, the framework efficiently identifies profitable arbitrage opportunities under realistic trading conditions. The proposed framework achieves an average detection latency of 0.002 milliseconds, providing empirical performance benchmarks for single-exchange cryptocurrency trading systems. Experiments on a six-month historical dataset yielded a detection accuracy of 92%, while additional validation on live cryptocurrency market data streams confirmed the framework’s real-time performance and low latency. This high-speed detection is crucial in high-frequency trading (HFT), where brief pricing inefficiencies can yield significant profits before being corrected. The experimental pipeline is designed to support reproducibility and comparative evaluation in applied FinTech research.
Novel framework and reference architecture for artificial intelligence models for stock markets Cancho-Rodriguez, Ernesto David; Cano Lengua, Miguel Angel
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11171

Abstract

This original research article proposes, as its contribution, a novel unified framework and reference architecture for stock market prediction in postCOVID financial markets, which exhibit unprecedented volatility and nonlinear dynamics, demanding more robust predictive approaches than traditional models can provide. This original framework integrates artificial intelligence (AI) and machine learning (ML) models, ranging from classical techniques support vector machine (SVM) to deep learning (DL) architectures such as long short-term memory (LSTM) neural networks and gated recurrent unit (GRU) models, within a modular system encompassing data ingestion, sentiment processing, predictive optimization, reinforcement learning (RL), and cloud-based portfolio management. Another key original contribution is the synthesis of standards (ISO 23053, ISO 38505, ISO 20546) with big data methodological frameworks (REBD and Biggy), forming a unified meta-framework that orchestrates predictive signals from sentiment analysis (SA) and macroeconomic indicators. Experimental realworld stock market validation on mining-sector stocks demonstrated, with a 100% success rate, consistent investment outperformance over passive Buy Hold baselines, yielding investment optimizations of up to +11.11 pp: the evaluated portfolios achieved 23.57% and 8.25% returns versus their 19.94% and 5.41% baselines, respectively. These results confirm the validity of the proposed novel framework as a reproducible reference architecture, an original contribution empirically grounded and experimentally validated for the development of future financial AI systems.
Securing electric vehicle charging stations from adversarial cyber attacks using hybrid detection models Jaladanki, Ravindra Babu; Kolluru, Pavan Kumar; Shaik, Nagul; Trinadh Naidu, Kamparapu V V Satya; Veeraiah, Duggineni; Pradhan, Anita; N. P. Sairam, Rallabandi Ch. S.
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11144

Abstract

Electric vehicle charging infrastructure (EVCI) has become essential. However, these infrastructures are increasingly vulnerable to cyber threats, particularly through spoofing and adversarial attacks on charging ports. This paper introduces a robust anomaly detection framework leveraging long short-term memory (LSTM) based autoencoders to identify anomalies in electric vehicle (EV) charging port current magnitudes. A simulated EVCI setup is developed in MATLAB/Simulink to capture charging behaviors under normal and adversarial scenarios. To generate adversarial data, the fast gradient sign method (FGSM) is employed. The reconstructed outputs from the LSTM-autoencoder (LSTM-AE) are statistically compared to real-time observations using the Kolmogorov–Smirnov (KS) test to detect anomalies. The framework achieves a high detection accuracy of 98.5%, demonstrating strong resilience against cyber-injected data anomalies and setting a foundation for enhanced EVCI cybersecurity.
Exploring mobile health service adoption among diabetic patients using an integrated UTAUT-PMT model Saputra, Ragil; Adhy, Satriyo; Yasin, Hasbi
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.9998

Abstract

Mobile health services (MHS) have emerged as vital tools for delivering accessible and cost-effective healthcare, particularly for managing chronic conditions such as diabetes. This study investigates the behavioral factors influencing MHS adoption by integrating the unified theory of acceptance and use of technology (UTAUT) and protection motivation theory (PMT) into a unified model. A total of 189 valid responses were collected through both offline surveys (23 diabetic outpatients from 4 clinics) and online surveys (166 members of an Indonesian diabetes forum). Structural equation modeling was used to test the proposed relationships. The results show that self-efficacy (SE), effort expectancy (EE), and perceived vulnerability (PV) significantly influence behavioral intention (BI), while BI strongly affects MHS usage behavior (UB). Conversely, perceived severity (PS) and response cost (RC) were not significant. The study offers a novel theoretical contribution by demonstrating how the interaction between cognitive and motivational factors affects technology adoption in healthcare. Practical implications are provided for app developers and policymakers to improve MHS engagement through personalized interventions.
Enhancing solar power generation efficiency through chaotic beetle swarm optimization for constant power generation Sreenivasan Ramachandran; Ramkumar Ravindran
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11199

Abstract

The necessity for more and cleaner sustainable energy sources to generate power is increasing because of the reduction of fossil fuel supplies and their negative impacts on the environment. This research addresses the requiring crucial for optimized solar power systems in the weather change issues. In the beginning, the photovoltaic (PV) system’s voltage is enhanced by dual stage XBoost converter (DSXBC) with little voltage stress and maximum voltage gain. Also, the radial bias function neural network (RBFNN)maximum power point tracking (MPPT) tracks the PV system’s uppermost power and its parameters are fine-tuned by chaotic beetle swarm optimization (CBSO) algorithm. By integrating chaotic dynamics within the optimization process, CBSO runs a robust and efficient approach to navigating the complex search space related with MPPT. The MATLAB tool is utilized to reveal the efficacy of developed approach for allowing constant power generation in solar power generation systems with efficacy of 99.74% and tracking efficiency of 98.9% in steady state condition, thereby enabling continuous power generation.

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