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Hybrid deep learning-based congestion prediction for intelligent traffic management in 5G/6G networks

10.11591/ijeecs.v43.i2.pp472-484
T. Anuradha , Giribabu Sadineni , Laxmi Pamulaparthy , M. L. M. Prasad , N. Vijay , Rajamahendravarapu Lakshmi Durga
Future-generation communication networks, including millimeter-wave local area networks, broadband wireless access systems, and emerging fifth- and sixth-generation (5G/6G) networks, require intelligent traffic management to satisfy stringent quality of service (QoS) requirements, including ultra-low latency, high reliability, and massive device connectivity. As network traffic becomes increasingly dynamic and heterogeneous, accurate congestion prediction is essential for preventing resource overloading, maintaining network slicing performance, and ensuring efficient resource utilization. This paper proposes a hybrid deep learning (DL)-based congestion prediction model that combines long short-term memory (LSTM) and support vector machine (SVM) techniques to capture temporal traffic characteristics while improving prediction accuracy. The proposed framework was evaluated through a one-week simulation involving heterogeneous devices operating under varying network conditions to assess its robustness and generalization capability. Experimental results demonstrate that the proposed model achieved an overall prediction accuracy of 93.23%, while also exhibiting strong performance in terms of specificity, recall, F-score, and computational efficiency. By accurately predicting network congestion before service degradation occurs, the proposed framework enables proactive traffic management and adaptive resource allocation. These findings demonstrate that the hybrid LSTM–SVM model provides an effective, reliable, and scalable solution for intelligent congestion prediction in next-generation 5G/6G communication networks.
Volume: 43
Issue: 2
Page: 472-484
Publish at: 2026-08-01

Advanced personal bankruptcy prediction using tree-based deep learning models

10.11591/ijeecs.v43.i2.pp640-650
Nhat Nguyen Minh , Duy Ngo Hoang Khanh
Due to the unstable economic conditions, worsened by the post-COVID-19 environment and persistent foreign wars in 2024, financial institutions have growing difficulties in accurately predicting customer default probability. This study examines the use of sophisticated tree-based deep learning and deep neural network models for forecasting personal bankruptcy. This research utilises a dataset of roughly 9,800 individuals from Vietnamese financial institutions, spanning from 2012 to 2022, to evaluate the efficacy of models including neural decision tree, deep forest, tabular convolutional neural networks (TBCNN), and neural oblivious decision ensembles (NODE). The results demonstrate that the Deep Forest model far surpasses its competitors, providing nearly flawless predicted accuracy and enhanced interpretability. The findings highlight the efficacy of tree-based deep learning and deep neural network models as effective instruments for financial risk management, especially in volatile and unpredictable economic environments.
Volume: 43
Issue: 2
Page: 640-650
Publish at: 2026-08-01

The next era of electrification: engineering adaptive energy infrastructures for a decarbonized society

10.11591/ijeecs.v43.i2.pp355-362
Tole Sutikno
A new era of electrification is reshaping electrical engineering, with adaptive energy infrastructures becoming critical foundations for achieving resilient, low-carbon, and sustainable energy systems. Rapid advances in renewable energy integration, power electronics, distributed energy resources, battery energy storage, electrified transportation, and intelligent energy management are transforming conventional power systems into flexible, interconnected, and resilient infrastructures. This editorial discusses the emerging paradigm of adaptive electrification, in which future electrical networks are expected to dynamically coordinate generation, energy storage, conversion, transmission, distribution, and consumption across increasingly decentralized environments. Beyond traditional objectives of efficiency and reliability, next-generation electrical infrastructures must address growing electricity demand, carbon neutrality, power quality, climate resilience, cyber-physical security, and energy accessibility. The editorial further highlights several promising research directions, including grid-forming power electronics, adaptive microgrids, intelligent battery management, solid-state energy conversion, digitalized power infrastructures, resilient hybrid AC/DC systems, and coordinated human–AI decision-making. Collectively, these technological advances position electrical engineering at the forefront of the global energy transition, emphasizing that future electrification requires not only cleaner energy sources but also adaptive, intelligent, and sustainable infrastructures capable of continuously evolving to meet societal, environmental, and industrial challenges while supporting reliable and equitable access to electricity.
Volume: 43
Issue: 2
Page: 355-362
Publish at: 2026-08-01

Improved sizing of a damped passive filter for harmonic attenuation using a teaching-learning-based optimization algorithm for an industrial site

10.11591/ijeecs.v43.i2.pp363-383
Brahimi Omar , Rabah Djekidel , Hadjadj Abdechafik , Sid Ahmed Bessedik
Based on measurements of current harmonics that adversely affect power quality in an industrial setting, and to ensure compliance with permissible levels according to IEEE 519, this paper aims to compare the effectiveness of conventional and optimized sizing of damped passive filter components to reduce total harmonic distortion (THD) and improve the power factor (PF). The optimization of passive filter element values relies on a powerful metaheuristic algorithm called teaching-learning-based optimization (TLBO). Simulation results show that the THD of current and voltage reach values of 17.34% and 8.61%, respectively, and decrease to very low values after implementation of the damped passive filter in the optimal case using the TLBO algorithm. The results also demonstrated a proportional relationship between the harmonic currents in the load and the overall increase in losses in the transformer. Optimizing the damped passive filter reduced harmonic distortion, improved the PF, and limited energy losses, thus confirming the superior ability of this algorithm to identify a very high efficiency solution.
Volume: 43
Issue: 2
Page: 363-383
Publish at: 2026-08-01

Production scheduling using a hybrid approach based on genetic algorithm and convergent random search

10.11591/ijeecs.v43.i2.pp534-546
Belbachir Djelloul , Kadri Boufeldja
The flexible job shop scheduling problem (FJSSP) is a highly complex combinatorial optimization problem widely encountered in modern manufacturing systems. Its complexity arises from the simultaneous determination of operation sequencing and machine assignment, making it significantly more challenging than the classical job shop scheduling problem (JSSP). Recent advances in hybrid metaheuristics and intelligent optimization methods have improved solution quality; however, achieving an effective balance between global exploration and local exploitation remains a critical challenge. In this paper, a novel hybrid metaheuristic approach combining a genetic algorithm (GA) and convergent random search (CRS) is proposed to address the FJSSP. The proposed method exploits the global search capability of GA to explore the solution space, while CRS is employed as an adaptive local refinement mechanism applied to elite individuals. This hybridization strategy enhances convergence speed and avoids premature stagnation. Extensive computational experiments are conducted on well-known benchmark instances, including Brandimarte and Kacem datasets. The results indicate that the proposed GA–CRS approach significantly improves the makespan compared to classical GA and PSO based methods. In addition, the algorithm exhibits faster convergence behavior, reaching high-quality solutions in fewer iterations. Statistical analysis using non-parametric tests confirms the superiority of the proposed method. These findings demonstrate that the proposed hybrid GA–CRS algorithm provides a robust and efficient optimization framework for solving large-scale and complex FJSSP instances, outperforming several state-of the-art approaches.
Volume: 43
Issue: 2
Page: 534-546
Publish at: 2026-08-01

Intelligent land use and land cover classification using Sentinel-2 multispectral imagery

10.11591/ijeecs.v43.i2.pp586-594
Neha Vyas , Koushik Sundar , Narayan Vyas
Accurate land use and land cover (LULC) classification is essential for environmental monitoring, agricultural planning, and sustainable resource management. This study investigates the effectiveness of Sentinel-2 multispectral satellite imagery for LULC classification by comparing the performance of three supervised classification algorithms: maximum likelihood classifier (MLC), minimum distance classifier (MDC), and neural networks (NN). Before classification, Sentinel-2 imagery underwent comprehensive preprocessing, including atmospheric correction, radiometric calibration, and cloud masking using ERDAS software to improve image quality and classification reliability. The Villupuram district of Tamil Nadu, India, was selected as the study area due to its diverse land cover characteristics. Classification performance was evaluated using overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), and the Kappa coefficient. Experimental results demonstrate that the MLC achieved the highest OA of 94.81% with a Kappa coefficient of 0.9308, outperforming MDC (90.65%, 0.8753) and NN (84.38%, 0.7917). These findings confirm that supervised classification of Sentinel-2 multispectral imagery provides reliable and accurate LULC mapping, offering valuable geospatial information to support precision agriculture, environmental monitoring, land resource management, and sustainable regional planning.
Volume: 43
Issue: 2
Page: 586-594
Publish at: 2026-08-01

Geospatial data processing and random forest-based intelligent system for regional investment readiness prediction

10.11591/ijeecs.v43.i2.pp651-661
Yudhinanto Cahyo Nugroho , Desmon Desmon , Hasbullah Hasbullah , Triyugo Winarko
This paper presents an intelligent system that integrates geospatial data processing and a random forest (RF) classification model to categorize regional investment readiness (IR). Regional investment planning is often constrained by fragmented socio-economic data, unequal infrastructure distribution, and unquantified disaster risk, which reduce the accuracy of decision making. To address this problem, multidimensional data consisting of socio-economic indicators, infrastructure accessibility, and disaster risk factors were collected from a sample of 15 administrative regions in Lampung Province, Indonesia, and processed through data cleaning, normalization, and feature selection. An IR score was first computed for each region using a weighted composite formula, then discretized into readiness classes and used as the target label to train a RF classifier capable of modeling complex nonlinear relationships among the input features. Given the limited sample size, model performance was evaluated using leave-one-out cross-validation, and classification metrics—accuracy, precision, recall, and F1-score—were reported to assess predictive reliability. The results reveal spatial disparities in IR, where regions with higher human development and better infrastructure tend to exhibit greater investment potential, while areas exposed to higher disaster risk tend to show lower readiness levels. The prediction outputs are integrated into a web-based interactive dashboard that enables spatial visualization and exploration of IR patterns. Given the small and single province sample, the proposed system should be regarded as a preliminary decision-support tool for policymakers and investors to help identify priority regions, and further validation on larger, more geographically diverse datasets is recommended to strengthen generalizability.
Volume: 43
Issue: 2
Page: 651-661
Publish at: 2026-08-01

Simulation of a frequency-reconfigurable multiband antenna for 5G and Wi‑Fi 6E/7 applications

10.11591/ijeecs.v43.i2.pp439-449
Ismahane Refsi , Miloud Benchehima , Mohammed Hicham Hachemi , Salah Eddine Brezini
This paper presents a compact frequency-reconfigurable multiband antenna design suitable for 5G and Wi‑Fi 6E/7 applications. Designed on a low-cost FR4 substrate, the antenna occupies a compact footprint of 28×15×1.6 mm, making it one of the smallest designs reported and suitable for integration into space-constrained devices. Its simple geometry enables easy design and integration across platforms. Frequency reconfigurability is achieved using PIN-diode-controlled reactive elements that manipulate current distribution to shift resonant frequencies. The design and optimization were performed using CST Microwave Studio to ensure accurate electromagnetic performance. Simulation results demonstrate that the proposed design supports up to eight distinct operating modes, highlighting its versatile frequency-reconfigurable capability. Among these modes, some achieve excellent performance at specific frequency bands including Wi‑Fi 6E/7 (2.4, 5 and 6 GHz) and 5G sub-6 GHz (3.5 GHz). These findings are confirmed with simulation results showing clearly that the antenna exhibits S_11 below –20 dB across the targeted frequency bands. The antenna also demonstrates satisfactory gain, with values above 1.5 dBi across the desired frequency bands. The antenna reveals a voltage standing wave ratio (VSWR) below 1.5 across the intended frequency bands. These characteristics make the proposed antenna a compact, versatile and efficient solution for current and future wireless communication systems.
Volume: 43
Issue: 2
Page: 439-449
Publish at: 2026-08-01

Coordinated multi-battery control for single-stage islanded AC microgrids

10.11591/ijeecs.v43.i2.pp384-399
Adhi Kusmantoro , Lukman Harun
Reliable power management is essential for islanded AC microgrids integrating photovoltaic (PV) generation and battery energy storage, particularly under variable solar irradiance and load conditions. This paper proposes a coordinated multi-battery control strategy using a single-stage AC-coupled configuration to ensure uninterrupted power supply while improving system reliability. The proposed topology employs two PV arrays: one directly connected to a battery inverter to supply the AC load and another dedicated to battery charging. Four battery units are coordinated through a fuzzy logic controller (FLC), which sequentially regulates battery discharge based on PV generation and load demand. Simulation studies were conducted under two operating scenarios. In the first scenario, at a solar irradiance of 1000 W/m², the PV system generated approximately 1200 W, and the proposed controller maintained power balance as the load demand increased. In the second scenario, when PV output decreased due to reduced solar irradiance or complete source interruption, the FLC coordinated battery operation at 0.07 s, 0.34 s, 0.64 s, and 0.93 s, ensuring continuous power delivery to the load. The simulation results demonstrate that the proposed coordinated control strategy effectively enhances power continuity, operational stability, and energy management in single-stage islanded AC microgrids.
Volume: 43
Issue: 2
Page: 384-399
Publish at: 2026-08-01

Factors that influence social media interaction among university students: emotional and mental well-being

10.11591/ijeecs.v43.i2.pp607-617
Nur Anis Mohd Jasmi , Fauziah Redzuan , Jasber Kaur Gian Singh
Social media usage is becoming popular among students, which is used both for academic and entertainment. However, the excessive use of social media can increase the level of stress, anxiety, and depression. The social media interaction can also lead the students to compare themselves with influencers. This study aims to identify the factors and the significant relationship between the factors of social media interaction and emotional and mental well-being. This research used a quantitative research and hypothesis-driven approach. The existing theories, such as social capital theory and social comparison theory were adapted for the online survey questionnaires. The collected data were analysed using partial least square-structural equation modelling (PLS-SEM) to analyse the two objectives of this research. Based on the findings, twelve (12) constructs were identified as the factors that contribute to social media interaction on university students' emotional and mental well-being by assessing and validating the constructs using PLS-SEM. Then, there are thirteen (13) out of twenty-three (23) hypothesis were significant relationships based on the final model testing using PLS-SEM bootstrapping method. Ten (10) paths of direct effect, two (2) paths of mediating analysis, and one (1) path of moderating analysis were significant relationships and supported the alternative hypothesis. It is recommended for future studies to also explore other analysis techniques, theories, or models, and the changes to other group such as older generations or working adults.
Volume: 43
Issue: 2
Page: 607-617
Publish at: 2026-08-01

OCTModNet: a deep learning-based framework for optical coherence tomography image multi-class classification

10.11591/ijeecs.v43.i2.pp495-506
Saja Ataallah Muhammed , Abbas M. Ali , Dler Salih Hasan
Sight is one of the most significant senses in human beings. Losing sight can change a human’s life dramatically. Early diagnosis and detection of retinal diseases will prevent sight loss. Optical coherence tomography (OCT) imaging is helpful in ocular imaging with its high resolution and non invasive imaging for diagnosing retinal diseases. This study proposes OCTModNet, a novel multi-class hybrid classification model that is capable of classifying seven different retinal diseases using a combination of publicly available datasets, Kaggle OCT-C8, and OCTDL datasets. For effective extraction of the significant features, the collected data is resized and normalized. Augmentation techniques are used to improve the training and learning process. For model development, we applied transfer learning and fine-tuned several state-of-the-art deep learning models, ResNet50, InceptionV3, DenseNet121, VGG16, and EfficientNetV2S. Two models that performed best, VGG16 and EfficientNetV2S, were selected as the model backbones with integration of a squeeze and excitation block. The results showed that the OCTModNet achieved an outstanding performance with 99% test accuracy, 99% precision, 99% recall, 99% F1-score, and an area under the curve (AUC) as 99% across the unseen data. These results point out the robustness and reliability of the proposed model to classify OCT images and have the potential to enhance clinical decision-making and assist ophthalmologists in the early detection of diseases.
Volume: 43
Issue: 2
Page: 495-506
Publish at: 2026-08-01

Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling

10.11591/ijai.v15.i4.pp3421-3430
Iwan Ady Prabowo , Hendro Wijayanto , Teguh Susyanto
Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Volume: 15
Issue: 4
Page: 3421-3430
Publish at: 2026-08-01

A comprehensive review of memory BIST algorithms for SRAM static fault detection

10.11591/ijeecs.v43.i2.pp400-412
Aiman Zakwan Jidin , Razaidi Hussin , Mohd Syafiq Mispan , Lee Weng Fook , Loh Wan Ying
Memory built-in self-test (MBIST) has become an essential design-for testability technique for ensuring the reliability and quality of embedded memories in modern integrated circuits. The effectiveness of an MBIST implementation is largely determined by its test algorithm, which defines the sequence of memory operations, directly influencing both test complexity and fault coverage. Designing an efficient test algorithm requires balancing low test complexity with comprehensive fault detection. This paper presents a comprehensive review of MBIST test algorithms for static fault detection in static random-access memory (SRAM). The review first summarizes the characteristics of major SRAM static faults and their corresponding detection requirements. It then compares representative test algorithms in terms of test sequence, computational complexity, fault coverage, and design methodology. Furthermore, the evolution of MBIST test algorithm development is discussed, ranging from conventional ad hoc approaches to enhanced algorithms derived from existing March tests. The comparative analysis indicates that an 18N-complexity test algorithm is generally required to achieve complete detection of all unlinked static faults in SRAM, whereas optimized 14N-complexity algorithms provide an effective trade-off between test time and fault coverage. Finally, the review identifies current research challenges, including efficient detection of dynamic and linked memory faults and the development of MBIST algorithms for emerging memory technologies such as magnetic random-access memory (MRAM), highlighting promising directions for future research.
Volume: 43
Issue: 2
Page: 400-412
Publish at: 2026-08-01

Affine-invariant feature learning for accurate ulcer detection in wireless capsule endoscopy images

10.11591/ijeecs.v43.i2.pp595-606
S. Bhuvaneswari , M. Sulthan Ibrahim
Ulcers are lesions that develop in the lining of the gastrointestinal (GI) tract, particularly in the stomach and small intestine, and may lead to severe complications such as Crohn’s disease and ulcerative colitis if not detected at an early stage. Conventional endoscopic procedures are often uncomfortable for patients and may provide limited visualization of the entire small intestine. Wireless capsule endoscopy (WCE) has emerged as a non-invasive alternative for comprehensive GI tract examination; however, automated ulcer detection from WCE images remains challenging due to image noise, complex tissue structures, and computational requirements. To address these issues, this paper proposes a Camargo’s Indexive Kuwahara filtering-based affine-invariant sliced regression (CIKF-AISR) framework for accurate and efficient ulcer detection. The proposed framework consists of image acquisition, preprocessing, segmentation, and feature extraction stages. Adaptive CIKF is employed to suppress noise while preserving edge information. Subsequently, Von Neumann locality segmentation combined with the Canberra distance measure is utilized to identify regions of interest (ROIs). Finally, affine-invariant saliency sliced regression extracts discriminative shape, color, and texture features for ulcer detection. Experimental evaluation on the Hyper-Kvasir dataset demonstrates that the proposed method achieves higher ulcer detection accuracy, improved precision, enhanced peak signal-to-noise ratio (PSNR), and lower detection time compared with existing deep CNN and VAE-GAN approaches. These results confirm the effectiveness of the proposed framework for computer-aided GI diagnosis.
Volume: 43
Issue: 2
Page: 595-606
Publish at: 2026-08-01

Evaluation of hybrid parallelism for scalable training of DenseNet-121 in diabetic retinopathy classification

10.11591/ijeecs.v43.i2.pp662-671
Indar Sugiarto , Djoni Haryadi Setiabudi , Darrell Cornelius Rivaldo , Taweesak Kijkanjanarat , Resmana Lim
Training large and complex deep learning models is often constrained by GPU memory limitations and prolonged training times. While several parallelism strategies have been proposed, this study specifically evaluates hybrid parallelism—a combination of data parallelism and pipeline parallelism—to address both challenges simultaneously. Using a case study on diabetic retinopathy (DR) classification with the DenseNet-121 architecture, we analyze the trade-off between computational efficiency and memory scalability. Results show that although hybrid parallelism does not yet provide speedup compared to a single-GPU setup—due to communication overhead and pipeline fragmentation—it enables training of large models that exceed the memory capacity of a single GPU. The trained model achieved a validation accuracy of 0.737, a quadratic weighted kappa (QWK) of 0.861, and a weighted F1-score of 0.749. In contrast, pure data parallelism showed a potential speedup of up to 1.9× in scenarios where the model still fits within a single GPU. These findings highlight the critical role of hybrid parallelism in overcoming the memory wall in large-scale model training, though optimization to reduce overhead remains a key challenge.
Volume: 43
Issue: 2
Page: 662-671
Publish at: 2026-08-01
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