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30,735 Article Results

Multiobjective framework for congestion management through coordinated scheduling of generation and demand

10.11591/ijece.v16i4.pp1688-1703
Jayesh Priolkar , Govind Kunkolienkar
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Volume: 16
Issue: 4
Page: 1688-1703
Publish at: 2026-08-01

A systematic review and conceptual roadmap for sky computing: AI-enabled orchestration, interoperability, and governance beyond multi-cloud

10.11591/ijece.v16i4.pp2247-2253
Abdullah Al-Bakri
The concept of sky computing is becoming known as an industry-independent “cloud of clouds” approach intended to address the issues of fragmentation inherent in today’s multi-cloud and hybrid cloud implementations. The objective of this systematic literature review is to examine the challenge of existing multi-cloud architectures, which despite delivering high reliability and purchasing flexibility, suffer from API heterogeneity, fragmented intercloud orchestration, insufficient workload mobility, and unresolved sovereignty and compliance challenges. Based on the PRISMA methodology, 325 papers released between January 2020 and June 2025 have been systematically selected in five scientific databases: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, and Scopus. Upon applying a process of elimination for duplicates, title-and-abstract screening, full-text evaluation, and quality assessment, a total of 35 peer-reviewed publications from the same timeframe have been thematically analyzed. Four thematic areas were examined: architectural architecture, intercloud orchestration, automation through artificial intelligence/machine learning (AI/ML), and security, privacy, and compliance. Not a single article predating the year 2020 was part of the final systematic literature review (SLR) database or bibliography. The results reveal that compatibility layers and intercloud brokers increase portability of workloads; scheduling based on artificial intelligence is useful in automating operations and achieving optimal cost performance; while zero trust architecture, self-sovereign identity, confidential computing, and policy-driven compliance are key in achieving trustworthy cross jurisdictional operations. The two major conclusions that arise from this study are: Firstly, future studies need to investigate explainability and energy-efficient AI orchestration in a real-world setting with multiple cloud providers, whereas secondly, cloud computing professionals need to incorporate principles of privacy, sovereignty, and compliance directly into the orchestration policies, not as an afterthought.
Volume: 16
Issue: 4
Page: 2247-2253
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

Lightweight face recognition based on PCA coupled with PSO-based feature optimization in resource-constrained environments

10.11591/ijece.v16i4.pp2134-2157
Chaimaa Khoudda , Zineb Gotti , Salma Azzouzi , Moulay El Hassan Charaf
We present a streamlined PCA-PSO structure of lightweight face recognition in this paper, which combines principal component analysis (PCA) to reduce dimensions with particle swarm optimization (PSO) to adaptively select features. In contrast to traditional PCA-based models, our model dynamically chooses the most informative subset of the 44 features and results in a small but informative representation. The approach has a high recognition accuracy of 99.99 on ORL and 98.45 on LFW, with low latency (approximately 3 seconds per epoch) and low memory footprint, and without the need to use a graphic card to run it. Extensive computational studies have shown that the proposed pipeline is much faster and consumes less memory than PCA-ACO and lightweight convolutional neural network (CNN) models like MobileNetV2 and Squeeze Net and is best suited to run in real-time in CPU-limited environments. The statistical tests prove the betterment of our method compared to the past methods, where the changes are statistically significant. This research offers an efficient, simple, yet scalable alternative to deep learning-based recognition systems, especially when embedded integration is a key requirement, by offering rapid convergence, adaptive feature selection, and compact representation.
Volume: 16
Issue: 4
Page: 2134-2157
Publish at: 2026-08-01

The life-cost cycle-based sizing of complementary energy storage technologies in DC microgrids

10.11591/ijece.v16i4.pp1724-1734
Dunya Sh. Wais , Huda A. Abbood , Radhi Sehen Issa
Hybrid energy storage system (HESS) configurations have the potential to mitigate the detrimental effects of photovoltaic power generation oscillations on DC microgrid safety and reliability. The economic efficiency of HESS can be improved through the utilization of batteries and their complementary attributes through an energy management strategy. This will allow for the full utilization of the benefits of superconducting magnetic energy storage (SMES), such as high efficiency, lossless energy storage, elevated power density, and rapid response. The battery-SMES HESS is subject to a life cycle cost (LCC) model, along with its associated constraints. System expenditures can be drastically cut by optimizing the HESS capacity layout. The goal function is the lowest LCC, presuming that the power demands of the system are met. Particle swarm optimization takes acceleration into account when designing the capacity of the system. In order to prove that the suggested method of configuring capacity works, a microgrid model is created and tested using numerical data.
Volume: 16
Issue: 4
Page: 1724-1734
Publish at: 2026-08-01

A comparative study of baseline convolutional neural network and ResNet50 for image-based tomato leaf disease classification

10.11591/ijece.v16i4.pp1876-1884
Sumana Budsabok , Wachiraporn Polpanumas , Piyanan Khongphai
Image-based techniques are widely used in plant disease classification to support agricultural productivity and facilitate early detection. This study presents a comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification. A publicly available dataset containing five categories—tomato bacterial spot, tomato late blight, tomato septoria leaf spot, tomato yellow leaf curl virus, and healthy leaves—was used in the experiments. Model performance was evaluated using several standard classification metrics, with emphasis on overall accuracy. The baseline CNN achieved an accuracy of 97.0%, whereas the ResNet50 model reached 99.6%. The results demonstrate that the ResNet50 model produces more stable and reliable predictions, particularly when distinguishing between visually similar disease classes. These findings confirm that transfer learning can effectively improve classification performance in plant disease recognition tasks.
Volume: 16
Issue: 4
Page: 1876-1884
Publish at: 2026-08-01

SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification

10.11591/ijece.v16i4.pp1985-1997
Sadly Syamsuddin , Jufri Jufri , Suci Rahma Dani Rachman , Suryani Suryani , Wilem Musu , Salmiati Salmiati , Yesycha Arun Mangopo
Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.
Volume: 16
Issue: 4
Page: 1985-1997
Publish at: 2026-08-01

Comparative performance analysis of lightweight face identification algorithm

10.11591/ijece.v16i4.pp2042-2060
Wuyun Wang , Suchada Sitjongsataporn
With the wide application of face recognition in resource-constrained scenarios like mobile and embedded devices, lightweight algorithms have become a research focus, but existing studies lack multi-dimensional, scenario-based performance comparisons. This paper studies the performance evaluation and application adaptation of lightweight face recognition algorithms, innovatively builds a scenario-based evaluation system, verifies the performance improvement of combining traditional algorithms with MobileNet, and constructs an efficient, stable and low-cost system. It elaborates on face recognition principles, including key links of face detection, feature extraction and matching, introduces traditional algorithms such as Eigenfaces, Fisherfaces and LBPH, and focuses on MobileNet’s characteristics: reducing computation and parameters via depthwise separable convolution, and adjustable width and resolution. Four comparative experiments verify the "traditional algorithms + MobileNet" hybrid strategy. Results show the combination achieves 98.1% accuracy, 4.3 percentage points higher than single MobileNet; LBPH + MobileNet balances performance and resource consumption best, with 110MB memory, 40% CPU usage and 315ms processing time. The hybrid strategy improves accuracy and efficiency in different scenarios, aiming to provide a scientific basis for the engineering application and subsequent optimization of lightweight face recognition algorithms, and supporting algorithm selection and performance improvement in resource-constrained scenarios.
Volume: 16
Issue: 4
Page: 2042-2060
Publish at: 2026-08-01

Developing a transdisciplinary design-based in-service science teacher training framework

10.11591/ijere.v15i4.38783
Joelash R. Honra , Ma. Kristina B. B Dela Cruz , Jermae B. Dizon-Yi , Raianne Joy V. Maulion , Sean Derrick M. Oliquiano , James C. Ollero , John Lorence A. Villamin
Contemporary science education requires teachers to facilitate learning that addresses complex, real-world problems beyond disciplinary boundaries. Yet, many in-service science teachers lack professional development that supports transdisciplinary problem-solving and innovative pedagogy. This qualitative study used a grounded theory (GT) approach to examine teachers’ experiences in a transdisciplinary, design-based training program and to develop a framework for effective professional learning. Participants engaged in sustained training grounded in design thinking and authentic problem contexts. Data were collected through semi-structured interviews, focus groups, reflective journals, training artifacts, and observations, and analyzed using constant comparative methods. Findings indicated shifts in teachers’ conceptions of problem-solving, enhanced capacity to integrate disciplinary and non-disciplinary perspectives, and changes in instructional planning and classroom practice. Design thinking functioned as a mediating process that helped teachers navigate ambiguity, collaboration, and iterative reflection. The resulting transdisciplinary design-based in-service science teacher training framework highlights key principles: authentic problem contexts, structured yet flexible design processes, collaborative inquiry, and iterative reflection. The study offers an empirically grounded framework with implications for teacher professional development, curriculum design, and policy.
Volume: 15
Issue: 4
Page: 2814-2822
Publish at: 2026-08-01

The relationship between arithmetic proficiency and artificial intelligence-assisted learning

10.11591/ijere.v15i4.39461
Khalid Marnoufi , Imane Ghazlane , Fatima Zahra Soubhi , Bouzekri Touri
Amidst the rapid developments witnessed in educational environments, this study aims to investigate the dynamic relationship between the desire for artificial intelligence (AI) supported learning and proficiency in mental arithmetic, considering the latter a decisive factor in enhancing cognitive acquisition. The study focused specifically on the academic elite, represented by students in the mathematical sciences track at the qualifying secondary level. To ensure the accuracy of the results, the methodology relied focusing particularly on the arithmetic subtest within the Wechsler intelligence scale for children as an effective tool for measuring logical reasoning and working memory. The target sample consisted solely of adolescents, who were characterized by a similarity and a homogeneity in their developmental stages and ages. Selection and analysis criteria were based on two pillars, the general scores obtained in the arithmetic subtest, and a systematic evaluation of the students’ aptitude and inclination toward using AI tools. The results concluded that there is a close correlation between arithmetic ability and the quality of logical reasoning in AI contexts. Furthermore, statistically significant homogeneity confirmed that students proficient in AI skills demonstrate higher levels of creative thinking and the ability to apply logic in learning.
Volume: 15
Issue: 4
Page: 3292-3300
Publish at: 2026-08-01

Flicker noise suppression and tuning range linearization techniques for RF CMOS voltage-controlled oscillators

10.11591/ijece.v16i4.pp1792-1804
Nam-Jin Oh
This paper proposes a differential RF CMOS voltage-controlled oscillator (VCO) employing a series LC (SLC) network to suppress 1/f³ flicker noise and linearize the tuning range. The SLC network incorporates two coupling capacitors connected to each node of a parallel inductor-varactor tank. Each series connection node is cross-coupled to the gates of switching transistors, facilitating a large signal swing. By optimizing the coupling capacitance, 1/f³ flicker noise is effectively mitigated. Designed in 180 nm CMOS technology, the proposed NMOS-only SLC VCO is compared with a conventional differential VCO using a parallel LC (PLC) network. Targeted for 3.3 GHz applications, the VCO maintains a consistent phase noise slope of −20 dB/decade across offset frequencies from 1kHz to 10 MHz. The SLC VCO achieves a phase noise of −71.6 dBc/Hz at a 1 kHz offset and −131.5 dBc/Hz at a 1 MHz offset with a power consumption of 11.9 mW from a 1.5 V supply. The resulting figure of merit (FOM) is 191.3 dBc/Hz at a 1 MHz offset.
Volume: 16
Issue: 4
Page: 1792-1804
Publish at: 2026-08-01

Miniaturized patch antenna for the S-band communication subsystem of the 3U University CubeSat

10.11591/ijece.v16i4.pp1913-1926
Nabil El Hassainate , Loubna Berrich , Nabil Benjelloun , Ahmed Oulad Said , Zouhair Guennoun
This paper introduces a miniaturized patch antenna for the reception module of the 3U University CubeSat in the S-band communications subsystem. In order to reduce the physical characteristics of the antenna (dimensions, mass) and achieve circular polarization (CP), as well as increasing its performances, two techniques are used: the first consists of introducing semicircle truncation on both sides of the square patch, and the second consists of modifying the ground plane with networks of symmetrical slots along the main axes (x,y). The fabricated antenna prototype has overall dimensions of 55×55×3.27 mm and a total mass of 20.59 g. The developed antenna spans the uplink band (2.025 to 2.110 GHz) for payload and telemetry operations. The designed antenna achieves a reflection coefficient below minus 10 dB across the target frequency band, along with a minus 3 dB axial ratio bandwidth that is well appropriate to space communication links. The comparisons of the prototype results to the simulation results using CST and HFSS provide close agreement of around 90%.
Volume: 16
Issue: 4
Page: 1913-1926
Publish at: 2026-08-01

A hybrid content and character feature method for SMS spam detection

10.11591/ijece.v16i4.pp2061-2073
Gertrude Selase Gosu , Edward Appau Nketiah , Li Wang , Joshua Fenuku , Xiaoya Xu
Short message service (SMS) spam remains a significant challenge due to its impact on user security and communication efficiency. This study proposes a hybrid spam detection model, convolutional neural network with content and character-based features (CNN-CCB), which integrates word-level features and character-level features, with handcrafted content-based and character-based features in a unified deep learning framework. Unlike conventional CNN and hybrid models that rely primarily on learned representations, the proposed approach incorporates structural features to enhance detection of short and noisy text patterns. Firstly, the text data were tokenized and processed through dual convolutional branches, while handcrafted features are fused to improve classification. Secondly, class weighting is applied to address data imbalance while maintaining predictive reliability. Finally, regularization techniques are employed to prevent overfitting. The experimental results show that CNN-CCB achieves high performance, with an accuracy of 0.997, precision of 0.988, recall of 0.998, and F1-score of 0.993, outperforming baseline models such as long short-term memory (LSTM) dan gated recurrent unit (GRU). The model demonstrates consistent performance across three datasets, indicating the model’s robustness and generalizability. These findings suggest that the proposed hybrid framework is effective for SMS spam detection and has potential applications in mobile security and real-time communication systems.
Volume: 16
Issue: 4
Page: 2061-2073
Publish at: 2026-08-01

Hierarchical inner outer LQR-PID controller based on high-order sliding mode observer for underwater remotely operated vehicle

10.11591/ijece.v16i4.pp1841-1852
Sedini Aicha , Mokhtari Abdellah
This paper presents a hierarchical inner–outer linear quadratic regulator-proportional integral derivative (LQR–PID) control architecture integrated with a high-order sliding mode (HOSM) observer for precise motion control of an underwater remotely operated vehicle (ROV) operating in uncertain and highly nonlinear environments. The proposed control strategy is structured into two coordinated layers: An inner-loop LQR controller for dynamic stabilization and disturbance attenuation, and an outer-loop PID controller for trajectory tracking and set-point regulation. The control system has been organized into a two-layer structure: The inner loop based on an LQR controller to manage fast dynamics and compensate for unmodelled underwater effects. The outer loop utilizes a PID controller to guaranty smooth tracking performance and maintain robustness against parameter variations. To deal with limited sensors, a HOSM observer is applied to estimate the states that cannot be measured and to handle uncertainties. By combining the PID–LQR controller with the HOSM observer, the system becomes more robust, faster in response, and more accurate than using either LQR–HOSM or PID control alone. Simulations with a comparative study between these scenarios show better tracking for PID_LQR_HOSM overall system under varying ocean currents, and tests on a 1-meter ROV confirm its effectiveness for advanced navigation and manipulation tasks.
Volume: 16
Issue: 4
Page: 1841-1852
Publish at: 2026-08-01

Beyond coal: optimization of hybrid floating PV–hydropower systems for Indonesia’s decarbonization

10.12928/telkomnika.v24i4.27858
Firsta; IPB University Zukhrufiana Setiawati , Tania; IPB University June , Muh; IPB University Taufik , Rudi; Tanjungpura University Kurnianto
Indonesia’s failure to meet the 23% renewable energy target, resulting in continued reliance on fossil fuels, necessitated a multi-criteria evaluation of hybrid energy systems. This study aimed to simultaneously minimize the levelized cost of energy (LCOE), reduce carbon dioxide (CO2) emissions (decarbonization), and maximize the renewable energy penetration by integrating a coal-fired power plant (coal), hydroelectric power plant (hydro), and floating photovoltaic (FPV) power plant at sites in western Java (West Java and Banten provinces). Thirteen configurations were evaluated using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 climate data (1991-2020) and hybrid optimization of multiple energy resources professional (HOMER Pro) simulations. The optimal coal hydro-FPV-Li-Ion hybrid configuration achieved 91.1% renewable energy penetration, 94.5% decarbonization, avoided ~4.1 million kg CO2 annually, delivered an LCOE of $0.1201/kWh with an internal rate of return (IRR) of 7.6%, return on investment (ROI) of 5.9%, and payback period of 8.7 years. This configuration outperformed the pumped hydro storage alternative, demonstrating 38% lower capital expenditure (CAPEX) and 49% faster payback period, confirming its economic suitability for Indonesia’s fiscal constraints. These findings indicate that a hybrid energy system with battery storage technology is a technically and economically viable pathway toward Indonesia’s decarbonization agenda.
Volume: 24
Issue: 4
Page: 1427-1439
Publish at: 2026-08-01
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