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30,938 Article Results

High-gain antenna arrays for millimetre-wave energy harvesting: architectures, challenges, and future directions

10.11591/ijai.v15.i3.pp2896-2906
Shalini Mirle Gajendra , Naveen Kalenahalli Bhoganna
The rapid expansion of fifth-generation (5G)/sixth-generation (6G) networks and internet of things (IoT) ecosystems has intensified the need for self sustaining power solutions to support billions of wireless devices. Millimetre-wave (mmWave) energy harvesting (EH) emerges as a viable alternative to traditional battery-powered systems, leveraging ambient radio frequency (RF) signals to provide continuous energy for IoT, smart sensor networks, and next-generation wireless applications. However, several challenges hinder its widespread adoption, including high path loss, low RF to-direct current (DC) conversion efficiency, and the trade-off between high gain and wide bandwidth. This paper presents a comprehensive review of high-gain mmWave antenna arrays, exploring state-of-the-art advancements in beamforming techniques, phased arrays, metasurface-enhanced rectennas, and multi-band EH architectures. We analyse existing methodologies, identifying key research gaps such as scalability constraints, material limitations, and real-world deployment challenges. Additionally, we highlight emerging trends, including artificial intelligence (AI)-driven adaptive beamforming, intelligent metasurfaces, and cost-effective fabrication techniques, which can significantly improve mmWave RF EH efficiency. By addressing these gaps, this study provides insights into future research directions for developing high-performance, scalable, and commercially viable mmWave EH solutions. The findings pave the way for the practical deployment of battery-free IoT devices, smart city infrastructures, and energy-autonomous wireless communication networks in the 6G era.
Volume: 15
Issue: 3
Page: 2896-2906
Publish at: 2026-06-01

Effects of sparse datasets on time interval-aware self-attention sequential recommendation models

10.11591/ijai.v15.i3.pp2761-2773
Weishan Ooi , Lee-Yeng Ong , Meng-Chew Leow
Recommendation models serve as crucial filters in managing information, yet they face a few crucial challenges, such as capturing user-item interaction behaviors in sparse datasets. Data sparsity refers to an issue where there is a lack of interactions or missing values in the recommendation dataset. A sparse dataset with a massive number of missing values and interactions leads to more dynamic user behaviors, which suffers a poor recommendation quality. The self-attention mechanism from Transformer can alleviate the effects of data sparsity in datasets by assigning weights to items of interaction behaviors. This allows the model to capture the user dependencies in complex user behavior, which is beneficial for sparse datasets with patterns that are not immediately apparent. This approach has shown its capability to handle large and sparse datasets, as seen in time interval-aware self-attention sequential recommendation model (TiSASRec). It utilized the self-attention mechanism, considering the timestamp and absolute positions of items to estimate the higher attention weights to show the importance of recent items. Thus, this study aims to investigate the effects of sparse datasets by comparing the performance of TiSASRec model with self-attention based sequential recommendation model (SASRec), which excludes time interval-awareness.
Volume: 15
Issue: 3
Page: 2761-2773
Publish at: 2026-06-01

Machine learning-enabled joint antenna selection and precoding

10.11591/ijai.v15.i3.pp2369-2376
Monica Nilesh Kalbande , Kanala Sai Madhuri , M. Venkateswara Rao , Saradha Rani Sabbavarapu , Rajyalakshmi Uppada , Lakshmi Durga Rajamahendravarapu
Joint antenna selection (AS) and precoding design is essential for improving spectral efficiency and energy efficiency in multi-antenna wireless communication systems. However, conventional optimization-based solutions rely on exhaustive search and iterative processing, leading to high computational complexity that limits real-time applicability. This work proposes a machine learning-enabled framework that shifts the computational burden from online operation to offline training. Optimal AS and precoding decisions are first generated offline using model-based optimization under diverse channel conditions. A supervised machine learning model is then trained to learn the relationship between channel state information (CSI) and optimal transmission configurations. During online operation, the trained model enables fast and efficient AS with significantly reduced processing time. Numerical results demonstrate that the proposed approach achieves near-optimal system performance while substantially lowering computational complexity, making it well suited for real-time and next-generation wireless communication systems.
Volume: 15
Issue: 3
Page: 2369-2376
Publish at: 2026-06-01

Residual reinforcement learning for disturbance-resilient control under modeling uncertainties

10.11591/ijece.v16i3.pp1175-1187
Abolanle Adetifa , Rexcharles Enyinna Donatus , Daniel Udekwe
Modern control systems must operate reliably in the presence of modeling uncertainties and external disturbances, conditions under which conventional fixed-gain controllers often exhibit performance degradation. This paper proposes a residual reinforcement learning framework for disturbance-resilient pitch-rate control of an aircraft longitudinal model. A classical proportional-integral-derivative (PID) controller is employed as a stabilizing baseline, while a deep deterministic policy gradient (DDPG) agent learns a bounded residual control signal to compensate for unmodeled dynamics and external perturbations. To promote favorable transient behavior, the learning process incorporates transient-aware and reference-model-based reward shaping, while actuator constraints are enforced within the environment dynamics. Simulation results demonstrate that the proposed residual controller achieves a superior balance between response speed, overshoot, and tracking accuracy compared with both the standalone PID controller and a pure DDPG-based controller. In particular, the residual architecture significantly reduces overshoot and tracking error while preserving fast transient response and providing robust disturbance rejection under large pitching moment disturbances. These results indicate that residual reinforcement learning offers a practical and effective approach for enhancing robustness and performance in safety-critical flight control applications.
Volume: 16
Issue: 3
Page: 1175-1187
Publish at: 2026-06-01

Vietnamese gender paradox: human rights education policy for sustainable development goal 4.7

10.11591/ijere.v15i3.38742
Xiem Nguyen Thi , Chu Thi Mai Huong , Tran Hanh Linh
The research evaluates whether gender predicts teachers’ perceived gender inequality (PGI) and whether employee experience (EX) mediates this association in Vietnam’s feminized teaching workforce. An online cross-sectional survey involved 283 in-service teachers nationwide, administered between March and August 2025. Sample adequacy was confirmed before data collection using a root mean square error of approximation (RMSEA-based) structural equation modeling (SEM) power approach. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) with 5,000-resample bootstrapping to test direct and indirect effects. Findings indicate that female teachers report higher PGI and less favorable EX than male teachers. EX strongly predicts perceived inequality and partially mediates the gender inequality relationship. The mediation pattern highlights practical evaluation targets in workload allocation, professional voice climate, and promotion feasibility. These results support substantive-equality reforms in school governance and institutionalized human rights education aligned with sustainable development goal (SDG) 4.7.
Volume: 15
Issue: 3
Page: 1941-1951
Publish at: 2026-06-01

Dynamic optimization using long short-term memory and genetic algorithms for predicting marine data

10.11591/ijai.v15.i3.pp2826-2837
Mukhlis Mukhlis , Indra Jaya , Sri Nurdiati , Karlisa Priandana , Irman Hermadi
This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.
Volume: 15
Issue: 3
Page: 2826-2837
Publish at: 2026-06-01

Artificial intelligence-based risk assessment in agro-industry using supervised neural networks

10.11591/ijai.v15.i3.pp2260-2268
Imam Santoso , Izzum Wafi'uddin , Naila Maulidina Lu'ayya , Annisa'u Choirun , Siti Asmaul Mustaniroh , Dodyk Pranowo , Ainur Rofiq
The coffee supply chain involves high production volumes, complex multi actor interactions, and increasing sustainability requirements, yet remains highly vulnerable to risks dimension. This study aims to develop and evaluate a decision-support framework that improves the accuracy and consistency of sustainability risk classification in the coffee supply chain. The proposed framework integrates failure mode and effect analysis (FMEA) with a supervised artificial neural network (ANN) using backpropagation (BP) to enable data-driven and adaptive risk assessment. Empirical data was collected from 55 respondents, resulting in the identification of 35 supply chain risk factors. These data were used to train and validate an ANN-based classification model implemented in a Python environment, with standard preprocessing and stratified data partitioning to ensure robustness. The ANN classified risks into five categories using supervised learning. The results demonstrate strong predictive performance, achieving overall accuracy of 98.97%, with precision, recall, and F1-scores exceeding 96.8% across all risk classes. Confusion matrix analysis confirms reliable generalization and minimal misclassification. The findings indicate that integrating FMEA with ANN-BP significantly enhances risk classification compared to conventional qualitative approaches. The proposed framework provides a scalable and reliable decision-support tool for dynamic risk scoring, supporting enhancement of sustainable practices in agro-industrial coffee supply chains.
Volume: 15
Issue: 3
Page: 2260-2268
Publish at: 2026-06-01

Modeling academic leadership in secondary schools: evidence from northeastern Thailand

10.11591/ijere.v15i3.39185
Dusadee Butburee , Nawee Udorn , Paitoon Puangyod
This study aimed to develop and empirically test a structural equation model of academic leadership among secondary school administrators in northeastern Thailand. Data were collected from 480 administrators using a structured questionnaire and analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM). The results indicate that leadership personality and organizational context significantly influence curriculum leadership and innovation culture, which subsequently shape academic leadership outcomes. The proposed model explains 73.8% of the variance in academic leadership, demonstrating strong explanatory power. These findings contribute to the literature by providing an integrated structural framework that highlights the interplay between leadership capacity and contextual support in enhancing instructional quality and school effectiveness. However, the findings should be interpreted with caution due to the cross-sectional design and reliance on self-reported data.
Volume: 15
Issue: 3
Page: 2001-2010
Publish at: 2026-06-01

Gender and academic-level variations in perceived effects of artificial intelligence on English majors’ critical thinking

10.11591/ijere.v15i3.36922
Mariam Mardia , Md. Mahadhi Hasan
Integrating artificial intelligence (AI) tools in English studies raises significant concerns about whether it would diminish critical thinking and cognitive skills. The research aims to analyze how English majors in Bangladesh perceive the impact of AI tools on their critical thinking skills with regard to gender and academic levels. A mixed-method approach was employed through a purposive sampling technique. Constructivist learning and the technology acceptance model (TAM) theories were used in the study. The research design employed two instruments: a survey administered to 245 students from the Bachelor of Arts (BA) and Master of Arts (MA) programs, and six in-depth interviews. The study analyzed quantitative data using descriptive statistics and an independent-samples t-test and qualitative interview data using thematic analysis. Key quantitative findings suggested that students widely recognized the usefulness of these tools across multiple academic areas, such as structuring writing, generating ideas, and goal setting (mean=4.31), indicating positive responses across all items. The t-test findings did not show statistically significant differences in gender or academic level; however, small effect sizes slightly favored male and MA students across all items. Additionally, AI tools helped students cope with cognitive stress by helping them meet deadlines. However, the interviewed participants expressed concern about ethical issues, including the potential for AI to plagiarize. Therefore, this study argues for a balanced approach to AI in education, highlighting its advantages while acknowledging its potential drawbacks and mitigating the challenges before implementation.
Volume: 15
Issue: 3
Page: 2459-2468
Publish at: 2026-06-01

A structural model of factors influencing the practicum effectiveness of mathematics pre-service teachers in Vietnam

10.11591/ijere.v15i3.38795
Le Thi Tuyet Hanh , Nguyen Thi My Hang
Teacher education in Vietnam has undergone significant transformation following the implementation of the 2018 General Education Reform. Within this reform context, the teaching practicum plays a crucial role in enabling pre-service teachers to connect pedagogical knowledge acquired at universities with authentic classroom practice. This study developed and validated a structural model examining the factors influencing the practicum effectiveness (PE) of mathematics pre-service teachers in Vietnam. Using a quantitative research design, data were collected from 290 final-year pre-service teachers through a 25-item survey instrument representing six latent constructs: student characteristics (SC), training curriculum and practicum management (TCM), mentor teachers at partner schools (MT), university-based teacher educators (UTE), practicum conditions and innovations (PCI), and PE. The sample size satisfied the requirements for structural equation modeling (SEM) based on recommended minimum ratios of observations to estimated parameters. Confirmatory factor analysis (CFA) and SEM were conducted using SPSS 26 and AMOS 24 to examine the reliability and validity of the measurement model and the structural relationships among the constructs. The findings indicate that mentor teachers and SC exert significant direct effects on PE, whereas institutional and structural factors do not demonstrate significant direct effects in the structural model. These results highlight the critical role of relational and human factors in shaping practicum outcomes within authentic teaching contexts, particularly in the Vietnam’s ongoing educational reform.
Volume: 15
Issue: 3
Page: 2513-2525
Publish at: 2026-06-01

Examination of social studies teacher candidates’ views on digital citizenship

10.11591/ijere.v15i3.38957
Ayşegül Çelik Geldi , Ebru Kamiş
This research aims to reveal prospective teachers’ understanding of the concept of digital citizenship. This study utilized a qualitative research model, employing phenomenological design to identify pre-service teachers’ understanding of digital citizenship. The study group consisted of 100 prospective teachers enrolled in the Social Studies Education Department of the Faculty of Education at a university in Türkiye during the 2025-2026 academic year, selected according to convenient sampling. A semi-structured interview form consisting of five questions was prepared. Content analysis was used in the analysis of the study’s data. Based on the research findings, it was determined that prospective teachers defined digital citizenship, digital ethics, digital security, digital bullying, and digital literacy. Finding reveal that participants defined digital citizenship across dimensions such as digital literacy, ethics, security, bullying and identity, though often superficially. Results indicate partial awareness but limited competencies, highlighting the need to strengthen teacher education programs in digital citizenship.
Volume: 15
Issue: 3
Page: 2041-2050
Publish at: 2026-06-01

Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey

10.11591/ijece.v16i3.pp1236-1248
Pang Wai Leong , Raymond Chia , Phang Swee King , Goh Hui Hwang , Chan Kah Yoong , Chung Gwo Chin
A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function’s requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated.
Volume: 16
Issue: 3
Page: 1236-1248
Publish at: 2026-06-01

Sub-X-band reconfigurable antenna network with graphene slots

10.11591/ijece.v16i3.pp1249-1260
Hassna Agoumi , Seddik Bri , Youssef El Amraoui , Adil Saadi
This paper presents the design and analysis of a graphene-slotted hexagonal microstrip patch antenna and its extension to a compact 4×4 planar array operating in the sub-X-band. The objective of this work is to demonstrate that graphene-based electrical reconfigurability can be extended from a single antenna element to an array configuration while improving radiation performance. The proposed antenna integrates graphene slots etched into the radiating patch, where reconfigurability is achieved by electrically tuning the graphene conductivity through an external gate voltage Vg. The single antenna operates around 9.4 GHz with an impedance bandwidth of 400 MHz and a peak gain of 6 dB. The design is then extended to a 4×4 array with an inter-element spacing of approximately 1.2 wavelengths. The array operates in the 9–10 GHz range, provides a bandwidth of 380 MHz, and achieves a maximum gain of 13.08 dB. The results confirm that graphene-enabled reconfigurability can be preserved at the array level without increasing structural complexity.
Volume: 16
Issue: 3
Page: 1249-1260
Publish at: 2026-06-01

Bioelectricity generation and physicochemical evolution of a substrate with sheep compost in microbial fuel cells in a high Andean area

10.11591/ijece.v16i3.pp1085-1096
Joel Colonio , Elvis Carmen , Arlitt Lozano , Alizze Colonio
The recovery of organic waste, such as sheep compost, is a key strategy for energy valorization. This study evaluated its potential as a substrate in microbial fuel cells (MFCs) using zinc (anode) and copper (cathode) electrodes and analyzed the evolution of its physicochemical properties, using soil samples from a high Andean area of the Chacapampa district, Peru. Two configurations of ground-mounted MFCs in series were compared: C1 (16 reactors of 400 g) and C2 (8 reactors of 800 g), maintaining a total mass of 6.4 kg. The C2 configuration was significantly more efficient, generating a median power of 819.53 μW, more than double the 380.92 μW of C1 (p=0.002). The final physicochemical analysis revealed that the process transforms the substrate, increasing electrical conductivity and phosphorus availability, although potassium decreased. It is important to note that due to the use of reactive metal electrodes, the system operates as a hybrid microbial-galvanic cell, where the zinc anode is consumed. It is concluded that sheep compost is an effective substrate and that consolidating the volume in fewer reactors optimizes electrochemical performance, although long-term environmental impacts regarding zinc accumulation must be monitored.
Volume: 16
Issue: 3
Page: 1085-1096
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01
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