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Predictive model based on machine learning to identify sleep-related health problems

10.11591/ijai.v15.i4.pp3888-3902
Laberiano Andrade-Arenas , Inoc Rubio Paucar , Margarita Giraldo Retuerto , Cesar Yactayo-Arias
Sleep quality has become a growing public health issue worldwide, mainly due to a lack of awareness about its long-term consequences. Despite existing strategies to address this problem, there remains a need for more effective approaches. In this study, an early detection model for sleep disorders was implemented using the extreme gradient boosting (XGBoost) algorithm, following the knowledge discovery in databases (KDD) methodology, which includes the phases of selection, preprocessing, transformation, data mining, and interpretation. A dataset extracted from the Kaggle platform in CSV format was used, consisting of 374 records. With an overall accuracy of 91.5%, a recall of 100% for the insomnia class, and a precision of 100% for sleep apnea, the proposed model demonstrated exceptional performance. It also received an area under the curve (AUC) of 0.909 and an average F1-score of 0.913. With a mean accuracy of 91%, a 95% confidence interval (0.89–0.94), and a p-value of 0.0012, cross-validation confirmed its robustness and showed a statistically significant change from the baseline model. The error rates remained within clinically acceptable ranges, confirming its applicability as a diagnostic support tool. Overall, the results demonstrate the effectiveness of the model in identifying patterns related to sleep disorders.
Volume: 15
Issue: 4
Page: 3888-3902
Publish at: 2026-08-01

Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems

10.11591/ijai.v15.i4.pp3518-3527
Thang C. Vu , Dung T. Nguyen , Minh T. Nguyen , Long Q. Dinh , Mui D. Nguyen
This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
Volume: 15
Issue: 4
Page: 3518-3527
Publish at: 2026-08-01

A hierarchical scholar expertise information system based on publication profiles and research records: a case in Universitas Diponegoro

10.11591/ijeecs.v43.i1.pp250-258
Eko Didik Widianto , Hadiyanto Hadiyanto , Teddy Mantoro , Raka Sindu Wardoyo , Muhamad Irham Maulana
One factor that boosts the reputation of higher education institutions (HEIs) in gaining competitive advantage is the research productivity of their scholars, including at Diponegoro University (Undip). However, existing studies focus on metrics and their measurement but lack discussion of systems for leveraging these metrics to promote these scholars’ expertise. This study proposes a scholars’ expertise information system at Undip based on their publications and research track records to bridge information between the campus and outside parties, namely industry, government, and society. It provides expertise searching and browsing facilities in Dewey’s hierarchy of subjects and the institutional structure hierarchy. Scholar publications and research data are retrieved using the science and technology index application program interface (SINTA API). The collected data included lecturer profiles, study programs, SINTA subjects, articles, research, community service, intellectual property rights (IPR), and books. It has been fully deployed and adopted online, presenting the expertise of Undip scholars based on their publications and research records. The finding shows that all pages perform well, with an average GTmetrix B grade and a performance score of 91%, a structure score of 88%, and a 1.8s loading time. With all the functionalities and performances of the system, this study contributes to developing an information system model to promote the expertise of university scholars, primarily based on their publications and research.
Volume: 43
Issue: 1
Page: 250-258
Publish at: 2026-07-01

FPGA based forest fires prediction system

10.11591/ijeecs.v43.i1.pp78-92
Faroudja Abid , Nouma Izeboudjen , Fatiha Louiz
This paper describes the idea of designing and implementing an field programmable gate array (FPGA) based system on chip (SoC) for forest fires prediction (FFP). The FFP in the proposed system is based on the decision tree (DT) algorithm implemented as an intellectual property (IP) in the FFP Zynq-SoC architecture that constitutes the processing part of a smart sensor node. This latter processes the collected meteorological data and takes decision locally at sensor node level; without having to send massive data to the base station for decision. The performance of the decision-tree software classifier in terms of accuracy and recall are about 75% and 0.88, respectively. The hardware implementation results of the DT -based forest fires prediction SoC show that the developed DT IP core is area and power efficient.
Volume: 43
Issue: 1
Page: 78-92
Publish at: 2026-07-01

Advanced state machine-sliding mode current control energy management for multi-source DC microgrid

10.11591/ijeecs.v43.i1.pp63-77
Hamza Rezigue , Mabrouk Khemliche , Samia Latreche , Badreddine Kanouni , Hamza Khemliche
This work addresses effective power management in a multi-source DC microgrid. An innovative energy management technique utilizing a state machine control (SMC) integrated with a sliding mode current control (SMCC) has been developed. This approach offers advantages through its equitable distribution of power among sources, storage devices, and demand loads, thereby optimizing the flow rate, discharge, and charge cycles of energy storage devices; additionally, it improves the response time of PEMFC power across various states in comparison to a conventional PI controller. The SMC-SMCC has proposed nine scenarios, categorized into three state of charge (SOC) situations, to fulfill a predetermined set of parameters for the operation of the DC microgrid. Simulation studies conducted with a precise model in MATLAB/Simulink have demonstrated that the proposed SMC-SMCC is proficient in achieving effective power sharing, rapid DC link voltage control in terms of stability, and maintaining the SOC within its constraints; additionally, the SMC-SMCC exhibits a quicker response time compared to conventional SMC-PI.
Volume: 43
Issue: 1
Page: 63-77
Publish at: 2026-07-01

Automated drone-assisted detection system for rice leaf pathologies a deep learning approach

10.11591/ijeecs.v43.i1.pp148-156
Erfan Rohadi , Cahya Rahmad , Septian Enggar Sukmana , Aida Sartimbul , Kismet Anak Hong Ping , Dimas Rosiawan , Ahmad Afifuddin Zakki
Early and accurate detection of plant diseases is vital for maintaining agricultural productivity. This study investigates an automated disease identification system specifically designed for the IR64 rice cultivar. By combining drone-captured aerial imagery (UAV) taken during the plant's vegetative stage with public datasets, we established a comprehensive training dataset. The study evaluates and compares four convolutional neural network (CNN) architectures, InceptionV3, ResNet50, EfficientNetV2S, and MobileNetV2, assessing their predictive accuracy and real-world computational efficiency. Our 10-fold cross-validation results indicate varying levels of inference speed and accuracy among the models. InceptionV3 and MobileNetV2 displayed the highest stability and minimal misclassification rates across multiple disease types. In contrast, the performance of ResNet50 and EfficientNetV2S fluctuated significantly depending on the detected pathogen. In conclusion, coupling UAV imagery with fine-tuned deep learning models provides a fast, scalable solution for continuous crop monitoring and precision agriculture.
Volume: 43
Issue: 1
Page: 148-156
Publish at: 2026-07-01

Smart panel design for renewable energy generation

10.11591/ijeecs.v43.i1.pp18-27
Rudi Syahputra , Nelly Safitri , Fauzan Fauzan , Yassir Yassir , Teuku Hasannuddin , Akhyar Akhyar , Radhiah Radhiah , Zulfikar Zulfikar
The aim of this study is to design and develop a smart panel module specifically for solar power generation, which includes three critical functions: the automatic transfer switch (ATS), the automatic main failure (AMF), and capabilities for remote monitoring and control. The ATS and AMF features employ Haiwell AT12MOT Ethernet PLC control equipment in conjunction with the Haiwell B7H Ethernet IoT cloud HMI, both designed for remote operation through an IoT system and integrated with the Haiwell cloud application. The developed PLC program interacts with HMI software that is created using NB designer. Inputs from the PLC are monitored and managed via the Haiwell cloud application, which connects with the relay designated as input for the Haiwell AT12MOT PLC. The resulting design interfaces with the PLC output located within an electrical panel specifically designed to handle industrial loads. This research results in a smart panel capable of operating in an industrial context with a power capacity of 2,200 VA. It is noteworthy that during automatic operations, load transfers between solar power systems and PLN (the national grid company of Indonesia) do not occur instantaneously; instead, there is a delay of 10 seconds as the system stabilizes back to normal conditions.
Volume: 43
Issue: 1
Page: 18-27
Publish at: 2026-07-01

Insights on routing and scheduling approaches in the IoT from the perspective of energy, QoS, and security–a systematic review

10.11591/ijeecs.v43.i1.pp335-344
Rajeshwari Kenchammana Hosakote Nanjappa , Manuvinakurike Narasimha Sastry Suma
The internet of things (IoT) has been conceptualized to bring more efficient and seamless connectivity to a large number of low-power and low-cost embedded devices. In the context of IoT, limited radio resources create new challenges, such as collisions and access conflicts. Another challenge arises in dealing with energy constraints, as battery-powered IoT sensors have limited energy capacity in the sensing layer. Consequently, routing mechanisms play a fundamental role in dealing with route optimization and reliable data transmission problems in the transport layer, whereas broadcast and link scheduling are also considered as appealing solutions for fulfilling the energy efficiency, collision-free transmissions and latency requirements in the IoT. Also, security vulnerabilities are hard to identify in the IoT perception and transport layer due to its ad-hoc network topological factors. Thus, deploying efficient routing schemes in IoT demands effective collaborative solutions for cost-effective and secure route formation with energy-aware scheduling performance, which were not, explored much in the past. This investigation thereby analyses the strengths and limitations of existing efficient routing and scheduling solutions in IoT and extracts the critical findings which could provide quick survey to many researchers in this application area.
Volume: 43
Issue: 1
Page: 335-344
Publish at: 2026-07-01

Analysis of OFDM and filter bank multicarrier with offset quadrature amplitude modulation for 5G communication: a comparative study

10.11591/ijeecs.v43.i1.pp127-138
Magda Yousef Mοhаmеd , Esraa M. Eid , Mohammed Abo-Zahhad , Ahmed Hassan Еldеib
Emerging applications for 5G and beyond require wireless communication systems with high spectral efficiency, low latency, reliable synchronization, and robust channel estimation techniques. This paper presents a comparative analysis between orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) under identical simulation conditions. The comparison is performed in terms of spectral efficiency, power spectral density (PSD), bit error rate (BER), peak-to-average power ratio (PAPR), and channel estimation performance. Simulation results show that FBMC/OQAM has higher spectral efficiency and significantly reduced out-of-band (OOB) emissions than OFDM due to its superior spectral containment. Moreover, FBMC/OQAM provides better channel estimation performance in the frequency-selective multipath fading environment. On the other hand, OFDM has lower computational complexity and better PAPR performance. The obtained results highlight the performance differences between OFDM and FBMC/OQAM systems and demonstrate the potential of FBMC/OQAM as a promising waveform candidate for future wireless communication systems.
Volume: 43
Issue: 1
Page: 127-138
Publish at: 2026-07-01

Deep Q learning algorithm for detecting DDoS attacks on IoT devices

10.11591/ijeecs.v43.i1.pp299-313
Lana Kamla Ahmed , Kayhan Zrar Ghafoor
The rapid expansion of internet of things (IoT) networks has heightened security risks, particularly regarding distributed denial of service (DDoS) attacks against devices with limited computing capacity. High detection accuracy is crucial for these resource-constrained environments, where false positives can disrupt legitimate traffic and false negatives allow attacks to persist. However, modern reinforcement learning (RL) and machine learning (ML) intrusion detection solutions often exhibit poor generalization due to static state representations. To address this, this paper proposes a deep Q-learning (DQL) framework that integrates K-means clustering directly into the RL action space. Unlike prior RL-based IDS models, our approach dynamically integrates clustering into the learning process, enabling adaptive state representation and improved generalization to unseen traffic patterns. The system is formulated as a Markov decision process where the agent optimizes a composite reward function based on accuracy, precision, recall, and F1-score. Evaluated on the N-BaIoT dataset using 10-fold cross-validation, the proposed method achieves a classification accuracy of 98.95% and a weighted F1-score of 98.73%, significantly outperforming traditional ML and RL baselines. These results demonstrate the framework's effectiveness as a scalable, adaptive solution for intelligent IoT DDoS detection.
Volume: 43
Issue: 1
Page: 299-313
Publish at: 2026-07-01

Dung-beetle-optimization algorithm-based P-I-D controller for a separately excited DC-motor

10.11591/ijeecs.v43.i1.pp93-102
Kerrache Soumia , Haidas Mohammed
Understanding the proportional, integral, and derivative (P-I-D) control system is crucial for optimizing its parameters to achieve the desired system performance. The proportional gain determines how aggressively the system responds to the error, while the integral gain helps to eliminate any steady state error. The derivative gain plays a role in stabilizing the system by damping out any oscillations caused by sudden changes in the error. P-I-D control is a widely used control technique in various engineering applications, including the control of DC-motors, which is refers to adjust the motor’s input voltage, current, speed or position in order to achieve a desired output. One approach to tuning P-I-D parameters is the ziegler nichols (ZN) methods, where the first one involves to plot the step response of the model’s open loop with its tangent line. The other method conists systematically increasing the gains until the system becomes unstable, and then adjusting the gains to find the ultimate gain and ultimate period. One of the main advantages of metaheuristic algorithms is their ability to quickly converge to near-optimal solutions without getting stuck in local optima. This is achieved by using a combination of exploration and exploitation strategies to efficiently search through the solution space. Within our study, we seek to incorporate the Dung-Beetle based optimization algorithm (DBO) to adjust the P-I-D controller for a separately excited DC-motor’s (SEDCM) speed control using MATLAB-software relies on the objective functions: the integral absolue error (IAE), the integral squared error (ISE) and the integral time absolue error (ITAE). The results obtained are compared in their best performances on rise time, settling time, overshoot, peak response and peak time.
Volume: 43
Issue: 1
Page: 93-102
Publish at: 2026-07-01

Electrical engineering in the era of autonomous intelligence: building sustainable, resilient, and self-evolving energy systems

10.11591/ijeecs.v43.i1.pp1-6
Tole Sutikno
Electrical engineering is entering a transformative era in which autonomous intelligence is becoming an integral component of modern energy infrastructures rather than merely an auxiliary computational tool. The convergence of advanced power electronics, renewable energy technologies, intelligent sensing, edge computing, artificial intelligence, and high-speed communications is enabling electrical systems to evolve from passive and centrally controlled networks into adaptive, resilient, and self-evolving ecosystems. This editorial discusses the emerging paradigm of autonomous electrical engineering, where future power systems are expected to perceive operating conditions, learn from historical and real-time data, predict disturbances, and autonomously optimize their performance while maintaining reliability, security, and sustainability. Beyond conventional objectives such as efficiency and stability, next-generation electrical systems must address increasing renewable penetration, distributed energy resources, electrified transportation, cyber-physical security, and climate resilience. The editorial also highlights several promising research directions, including AI native power system operation, autonomous microgrids, digital twins, physics-informed intelligence, trustworthy and explainable AI, intelligent power electronic converters, and coordinated human–AI decision-making. These developments position electrical engineering as a foundational discipline for achieving sustainable development and future energy transition, while emphasizing that autonomous intelligence should augment engineering expertise to create safer, more reliable, and environmentally responsible electrical infrastructures.
Volume: 43
Issue: 1
Page: 1-6
Publish at: 2026-07-01

Coral classification in underwater images using a dual-branch deep learning framework

10.11591/ijeecs.v43.i1.pp207-218
Pracharat Sa-ngadsup , Chawan Koopipat
Automated coral classification from underwater imagery is essential for large-scale reef monitoring but remains challenging due to color attenuation, illumination variability, and differences between texture-focused close- range images and morphology-focused colony-level observations. To address these challenges, this study proposes a dual-branch convolutional neural network that integrates information from the CIELAB (LAB) color space with structural descriptors derived from the discrete wavelet transform (DWT). RGB images are first converted to the LAB color space to separate luminance and chromatic components in separate channels, enabling the model to exploit color and lightness information more explicitly. Structural information is extracted from the luminance channel using wavelet decomposition to capture high-frequency morphological patterns. The two representations are processed through parallel convolutional branches and fused at the feature level for classification. Experiments conducted on a unified coral dataset containing texture dominant and morphology-focused imagery across 14 classes show that the proposed method achieves 96.52% accuracy on the texture-focused RSMAS dataset and 88.33% accuracy on the morphology-focused structure RSMAS dataset, reducing the cross-domain performance gap from 22.46% to 8.19% compared with RGB baselines, demonstrating improved cross domain robustness for coral classification under heterogeneous underwater imaging conditions.
Volume: 43
Issue: 1
Page: 207-218
Publish at: 2026-07-01

A hybrid CNN-autoencoder-SVM/XGBoost model for polyphonic orchestral instrument classification

10.11591/ijeecs.v43.i1.pp114-126
Kelvin Wyeth , Iman Herwidiana Kartowisastro
Polyphonic orchestral recordings pose significant challenges in music information retrieval (MIR) due to their overlapping frequency ranges and timbral similarities among instrument families, which complicate multi-label instrument classification. Prior studies have explored the integration of convolutional neural networks (CNN)-based feature extraction with classical machine learning (ML) classifiers, often on monophonic or simpler datasets like IRMAS. But the integration of deep learning (DL) feature extraction, Autoencoder (AE)-based dimensionality reduction, and ML classifiers for polyphonic orchestral instrument recognition remains underexplored. This study proposes a hybrid framework utilizing a pre-trained Inception V3 CNN for feature extraction from mel-spectrograms, followed by an optional 50% dimensionality reduction via AE, and finally, classification with support vector machines (SVM) or extreme gradient boosting (XGBoost). Experiments were run on two polyphonic datasets, OpenMIC-2018 and Orchset. The results demonstrate that non-AE configurations generally outperform AE variants. These results extend prior studies such using polyphonic datasets. The results highlight the practical value of hybrid CNN-ML pipelines and the trade-offs of feature compression in MIR.
Volume: 43
Issue: 1
Page: 114-126
Publish at: 2026-07-01

User-centered requirements elicitation for explainable AI transparency in recommendation and advertising systems

10.11591/ijeecs.v43.i1.pp271-280
Osama Dakhel Alsuhaimy , M. Rizwan Jameel Qureshi
Modern recommendation and advertising systems increasingly rely on complex artificial intelligence (AI) models whose opaque decision-making processes limit user understanding and trust. While explainable artificial intelligence (XAI) techniques aim to improve transparency, excessive disclosure can increase privacy concerns and psychological discomfort. This study addresses the lack of structured approaches for regulating transparency in user-facing AI systems. We propose the optimal transparency and psychological safety (OTPS) framework, which regulates explanation depth, timing, and user control to balance interpretability with psychological safety. The framework is implemented through a modular architecture consisting of an explanation generation module, transparency controller, and user interface layer. A user survey involving 35 participants was conducted to evaluate perceptions of transparency, trust, and psychological comfort. Statistical analysis, including reliability testing and response distribution evaluation, indicates strong user preference for adaptive transparency mechanisms. The results demonstrate that regulated transparency improves user trust and usability without introducing significant system overhead, providing practical design guidance for explainable AI systems in recommendation and advertising platforms.
Volume: 43
Issue: 1
Page: 271-280
Publish at: 2026-07-01
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