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Correlates and predictors of generative artificial intelligence adoption among Filipino college teachers

10.11591/ijaas.v15.i3.pp998-1005
Almer B. Gamboa , John Paul P. Miranda
Filipino college teachers in public higher education institutions (HEIs) are increasingly expected to integrate generative artificial intelligence (GenAI) tools into their practice. Evidence on what drives this adoption in Philippine public universities, however, remains scarce. This study used a quantitative, cross-sectional correlational design to examine the levels, relationships, and predictors of GenAI adoption among 301 teachers from two public HEIs in Pampanga, Philippines, guided by the unified theory of acceptance and use of technology (UTAUT). Teachers reported strong agreement across all UTAUT constructs, with effort expectancy receiving the highest rating and facilitating conditions the lowest. Performance expectancy (β = 0.406, p < 0.001) was the strongest predictor of behavioral intention, followed by social influence (β = 0.295, p < 0.001) and effort expectancy (β = 0.257, p < 0.001), which together explained 65% of the variance in intention. Facilitating conditions did not significantly predict behavioral intention but, together with behavioral intention (β = 0.629, p < 0.001), predicted actual use (β = 0.266, p < 0.001). Teachers are more likely to adopt GenAI when they see clear instructional benefits and when peers and administrators encourage its use; sustained practice depends on whether institutions can provide the necessary infrastructure and training to support it. Public HEIs are urged to invest in AI literacy programs, strengthen peer and administrative endorsement, and address gaps in information and communication technology (ICT) infrastructure to support responsible and lasting GenAI integration.
Volume: 15
Issue: 3
Page: 998-1005
Publish at: 2026-09-01

Optimization-enabled machine learning with feature selection for phishing detection system

10.11591/ijaas.v15.i3.pp1131-1146
Lukman Adebayo Ogundele , Julius Temitayo Adepoju , Femi Emmanuel Ayo , Idayat Abike Akano , Abidemi Emmanuel Adeniyi , Halleluyah Oluwatobi Aworinde , Oluwasegun Julius Aroba , Gbohunmi Ajibesin
The rapid evolution of phishing methods has long been a threat to cybersecurity that demands adaptive and intelligent detection techniques. This current work envisions an improved phishing detection system that involves the integration of machine learning (ML) with feature selection for improving the performance of real-time classification. A comprehensive dataset was optimized and preprocessed through Chi-square (CHI) feature selection to reduce dimensionality while preserving the most discriminative features. Random forest (RF) algorithm, selected for its robustness, interpretability, and high computational efficiency for binary classification, was employed and achieved an accuracy of 96.25%, effectively identifying sophisticated phishing attacks. The architecture is designed to operate on large datasets with minimal computational overhead but with high precision and scalability. Experimental results also indicate the dependability of the system with an area under the curve (AUC)-receiver operating characteristic (ROC) score of 1.0, indicating perfect separation of phishing from normal cases. In addition, an adaptive classification model was used to accommodate unknown data sets with a 100% accuracy score. The results above identify the potential of adaptive, feature-optimized ML systems in the ability to handle dynamic phishing attacks and enable proactive cybersecurity defense.
Volume: 15
Issue: 3
Page: 1131-1146
Publish at: 2026-09-01

Advanced encryption standard with asymmetric key exchange for text encryption

10.11591/ijict.v15i3.pp1263-1271
Ravindra K Reddy , Vijayalakshmi P
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Volume: 15
Issue: 3
Page: 1263-1271
Publish at: 2026-09-01

Real-time selective harmonic elimination in multilevel inverters using embedded metaheuristic optimization on PYNQ-Z2

10.11591/ijpeds.v17.i3.pp1914-1925
Taha Ahmad Hussein , Dahaman Ishak
This paper offers a structured methodology for regulating optimal control switching angles in multilevel inverters to achieve selective harmonic elimination (SHE) using embedded optimization algorithms on the PYNQ‑Z2 FPGA operating base. The proposed approach employs metaheuristic techniques including particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimization (GWO), slime mould algorithm (SMA), and whale optimization algorithm (WOA) to generate candidate switching angles across a wide range of modulation indices. For each modulation index, the most effective solution towards harmonic reduction and waveform quality is selected and implemented on the FPGA controller, enabling reliable real‑time operation with very low delay. Experimental validation of a 31‑level single‑phase inverter confirms the effectiveness of the method in eliminating selected harmonics and refining the fundamental output. Combining comparative algorithmic selection with FPGA‑based control demonstrates a systematic and efficient strategy for advanced inverter systems. A MATLAB/Simulink model is constructed to represent the 31-level inverter to enhance the practical aspect and study the frequency spectrum, where many harmonics that contribute to obtaining THD within the IEEE standards are eliminated. Also, the different types of losses are calculated based on the governing mathematical rates.
Volume: 17
Issue: 3
Page: 1914-1925
Publish at: 2026-09-01

Design of a novel single-source 15-level inverter with self-balancing capacitors and sensorless PWM control

10.11591/ijpeds.v17.i3.pp1950-1961
Taoufiq El Ansari , Ayoub El Gadari , Youssef Ounejjar
Multilevel inverters (MLIs) are widely used in energy-conversion systems because they generate high-quality AC voltages with reduced harmonic distortion. However, existing MLIs often require numerous power devices, multiple DC sources, voltage sensors, or dedicated capacitor-balancing controllers, which increases both hardware cost and control complexity. This paper proposes a single-phase 15-level inverter derived from the Packed U-Cell structure. It employs a single DC source, three capacitors, and a reduced number of power switches. Based on the number of power switches, gate drivers, diodes, capacitors, DC sources, output levels, and total standing voltage per unit (TSVpu), the proposed topology achieves a lower cost function than the compared topologies reported in the literature. The proposed topology achieves a relatively low total standing voltage per unit (TSVpu) of 4.43. Sensorless open-loop SPWM offers inherent capacitor-voltage self-balancing, eliminating the need for voltage sensors or additional balancing loops. MATLAB/Simulink validation at 2 kHz and a modulation index of 1 covers steady-state operation, load transients, nonlinear loading, and DC-source voltage fluctuations. For a 50 Ω–20 mH load, the current THD is 2.20% without an output filter, confirming suitability for energy-conversion systems.
Volume: 17
Issue: 3
Page: 1950-1961
Publish at: 2026-09-01

THD reduction in a 13-level multilevel inverter using the angle control technique

10.11591/ijpeds.v17.i3.pp1982-1993
Dewan Ashikur Rahaman , Aive Alamgir , Md Ashraf Hossain , Tapan Kumar Chakraborty , Raisul Islam Rafi , Raiyan Rahman Zihad , Md. Abdullah Al Mahmud , Samia Sarkar
When compared to a traditional multilevel inverter, multilevel inverters can produce switched waveforms with lower amounts of harmonic distortion. Due to their capacity to provide excellent output waveforms at lower switching frequencies and without distortion, multilevel inverters have recently attracted more attention. The dynamic voltage restorer's multilevel topology reduces the output waveform's harmonic distortion without causing inverter power output losses. This study examines the most widely used topologies to determine how the sinusoidal switching angle affects total harmonic distortion (THD%). Among the crucial multilevel topologies, the cascaded H-bridge was selected. Because it needed fewer parts than the others. Asymmetric was selected for observation and simulations of output voltages up to Fifteen levels for equal and sinusoidal angles using PSIM software after a review of the literature. Appropriate switching angles were maintained in this research. With fewer H-bridges, higher-level multilevel inverters have been manufactured.
Volume: 17
Issue: 3
Page: 1982-1993
Publish at: 2026-09-01

Implementation of filter-clamped topology for transformerless three-phase PV inverters with reduced leakage current

10.11591/ijpeds.v17.i3.pp1553-1563
Mohammed Abdullah , S. Venkata Padmavathi
The growing prevalence of grid-connected photovoltaic (PV) systems has intensified the demand for efficient, reliable, and compact power conversion technologies. Transformerless photovoltaic (TLPV) systems provide improved efficiency and reduced size and costs compared to those using line-frequency or high-frequency isolation transformers. However, these inverters commonly exhibit leakage current because they lack galvanic isolation. Conventional topologies address this issue by adding extra semiconductor devices or employing complex control methods. This paper examines how a filter-clamped inverter (FCI) operates in grid-connected three-phase transformerless photovoltaic (TLPV) applications. However, the proposed inverter successfully reduces leakage current without additional components or control modifications. This makes it a compact and simplified alternative for transformerless PV applications. Additionally, the traditional full-bridge (FB) inverter with three phases, when compared to the FC inverter, has very high leakage current. Simulation results validate the FC inverter’s ability to minimize leakage current.
Volume: 17
Issue: 3
Page: 1553-1563
Publish at: 2026-09-01

A novel machine learning based maximum power point tracking in interleaved buck-boost converter for portable solar-powered electric vehicle charging in rural areas

10.11591/ijpeds.v17.i3.pp2247-2258
C. Niranjana , P. K. Vineeth Kumar , J. J. Jijesh , G. B. Arjun Kumar , Dileep Reddy Bolla , K. N. Sunil Kumar
Need for off-grid electric vehicle (EV) charging solutions, using intelligent control systems, such as machine learning (ML)-based maximum power point tracking (MPPT), to harness solar energy, offers a means of optimizing efficiency even in the face of fluctuations. This innovative strategy combines clean energy, cutting-edge power electronics, and practical application, which makes it perfect for fostering innovation in areas with inadequate infrastructure. For rural areas without grid infrastructure, this paper presents a novel design and performance assessment of a portable solar-powered EV charging system. To maximize solar energy harvesting and charging efficiency, the system combines an interleaved buck-boost converter with an ML-driven MPPT algorithm. It is appropriate for small electric vehicles (EVs) like auto rickshaws because it uses a 48 V lithium iron phosphate (LiFePO₄) battery. A supervised regression model trained on real-time electrical (voltage, current, and power) and environmental (temperature, irradiance) parameters is used to implement the MPPT algorithm. The system was created using MATLAB/Simulink, and the key performance parameters were evaluated using real-time information. Analyses of the key performance metrics like charging efficiency, converter stability, and tracking accuracy show a superior energy harvesting efficiency of 97%.
Volume: 17
Issue: 3
Page: 2247-2258
Publish at: 2026-09-01

Digital twin driven federated multi-agent intelligence for autonomous renewable forecasting and smart grid optimization

10.11591/ijpeds.v17.i3.pp2029-2038
Pushpa Sreenivasan , K. Gattaiah , N. Hemalatha , Radhey Shyam Meena , Mallareddy Adudhodla , V. S. Bhagavan
The rapid growth of renewable energy integration has increased the complexity of smart grid operation due to the intermittent nature of distributed energy resources and continuously varying load demand. Existing approaches often rely on centralized control or combine only selected intelligent technologies, limiting scalability, data privacy, and autonomous decision-making. This paper proposes a digital twin-driven federated multi-agent intelligence (DT-FMAI) framework that integrates virtual system synchronization, privacy-preserving distributed learning, and cooperative multi-agent control within a unified architecture. Digital twins continuously mirror physical grid assets, federated learning enables collaborative forecasting without sharing raw data, and intelligent agents coordinate energy management in real time. The framework was implemented in MATLAB/Simulink with TensorFlow Federated and evaluated using renewable generation, weather, battery, and load datasets. Results demonstrate a 15-25% reduction in forecasting error, 10-18% improvement in voltage regulation, 92-96% load-matching efficiency, and 12-20% higher energy efficiency. These outcomes demonstrate the potential of the proposed framework for scalable, secure, and intelligent renewable-integrated smart grid operation.
Volume: 17
Issue: 3
Page: 2029-2038
Publish at: 2026-09-01

Comparison of acrylic and zinc blade materials on the performance of Darrieus vertical axis wind turbine

10.11591/ijpeds.v17.i3.pp2162-2171
Dina Maizana , Andreas Tiopan Togatorop , Indri Dayana , Habib Satria , Muhammad Irwanto Misrun , Yanawati Yahya
The performance of vertical axis wind turbine is strongly influenced by blade characteristics, yet the effect of blade material on turbine performance remains relatively underexplored, particularly for small scale Darrieus type turbines. This study experimentally investigated the influence of acrylic and zinc blade materials on the aerodynamics and energy conversion performance of a three bladed Darrieus vertical axis wind Turbine. A laboratory-scale experimental setup was employed to evaluated turbine rotational speed, torque, and electrical power under a controlled wind speed of 4.3 m/s. The results demonstrate the blade material significantly affects the turbine’s ability to extract and convert wind energy. The acrylic blade turbine achieved a power coefficient (Cp) of 0.293 and an overall conversion efficiency 62.81%, compared with 0.208 and 47.11%, respectively, for the zinc bladed turbine. The acrylic configuration therefore provided approximately 25% higher overall performance than zinc configuration. These findings experimentally validate the importance of blade material selection in the design of small scale Darrieus wind turbines and indicate that acrylic can provide a more effective alternative to zinc for improving wind energy conversion under low speed operating conditions. The results provide practical guidance for material selection in the development of lightweight, low speed vertical axis turbines for decentralized and small scale renewable energy applications.
Volume: 17
Issue: 3
Page: 2162-2171
Publish at: 2026-09-01

Deep learning-based intelligent islanding detection for grid-connected photovoltaic systems using convolutional neural networks

10.11591/ijpeds.v17.i3.pp1755-1767
Dondapati Ravi Kishore , T. Vijay Muni , K. Venkata Kishore , V. Suresh , S. Saahithi , Thandava Krishna Sai Pandraju , S. N. Chaitra , B. Logeshwary
The increasing integration of photovoltaic (PV) systems into smart grids requires fast and reliable islanding detection to maintain grid stability and operational safety. Conventional detection methods often face challenges such as delayed response, reduced accuracy, and large non-detection zones under varying operating conditions. This paper proposes an intelligent islanding detection method for grid-connected PV systems using advanced artificial intelligence and deep learning techniques. Electrical parameters including voltage, current, frequency, and power signals are analyzed using signal processing methods and classified through a convolutional neural network (CNN) model developed in MATLAB/Simulink. Simulation results demonstrate that the proposed AI-based approach achieves rapid and accurate detection of islanding events with improved sensitivity and reduced false detections compared to conventional techniques. The proposed framework enhances the reliability, safety, and protection performance of modern photovoltaic power systems integrated with smart grids.
Volume: 17
Issue: 3
Page: 1755-1767
Publish at: 2026-09-01

Toward energy-efficient AGVs: A review of mechanical design contributions and optimization framework

10.11591/ijpeds.v17.i3.pp2070-2085
Amizi Noor , Khairur Rijal Jamaludin , Wan Zuki Azman Wan Muhamad , Faizir Ramlie , Nolia Harudin
Energy optimization in automated guided vehicles (AGVs) is critical for improving efficiency and sustainability in intralogistics systems, particularly in path planning and scheduling applications. Various optimization approaches have been proposed from operational, computational, and energy supply perspectives. Although energy supply technologies offer advantages in energy storage and recovery, their integration into AGV systems remains limited due to technological maturity and implementation challenges. Consequently, control-based approaches, including artificial intelligence, have become dominant optimization strategies. While these methods improve operational performance, they also increase computational energy demand, highlighting the need for alternative approaches to reduce baseline power consumption. This paper reviews AGV energy optimization studies while emphasizing the potential of mechanical design as an alternative optimization scope. The review reveals that mobility inefficiencies such as slip, skid, and instability are commonly mitigated through control strategies rather than resolved at their mechanical source. To address this gap, a Taguchi-based mechanical optimization framework is proposed for evaluating multiple mechanical factors and parameter levels. The framework aims to reduce baseline power demand and minimize reliance on computationally intensive control strategies, contributing toward more energy-efficient AGV systems.
Volume: 17
Issue: 3
Page: 2070-2085
Publish at: 2026-09-01

Transformer fault diagnosis using dissolved gas analysis: a hybrid ensemble model with data preprocessing

10.11591/ijpeds.v17.i3.pp1780-1793
Fatima Zohra Boudjella , Souhila Boudjella , Nasiru Yahaya Ahmed , Hazlee Azil Illias , Smail Latifa , Reriballah Hafidha
Failures and guaranteed dependability of the electrical grid, early fault diagnosis in power transformers is essential. By examining gas ratios suggestive of faults, dissolved gas analysis (DGA) continues to be a vital component for transformer health monitoring. Using four preprocessing techniques raw data, min-max normalization, logarithmic transformation, and square root transformation; this study suggests a machine learning method for fault detection using DGA gas ratios (such as CH₄/H₂, C₂H₂/C₂H₄). Random forest (RF), support vector machines (SVM), gradient boosted trees (GBT), and a hybrid RF-GBT model that uses prediction fusion were the four supervised classifiers assessed. Performance was assessed using accuracy, precision, recall, f1-score, and Cohen's kappa. Experimental results show that the hybrid RF-GBT model with logarithmic transformation achieves the highest performance, with 94.93% accuracy and 92.37% Cohen's kappa, significantly outperforming individual classifiers. Data preprocessing, particularly logarithmic and square root transformations, enhances diagnostic robustness by mitigating feature skewness. This study underscores the importance of tailored preprocessing and ensemble methods for reliable transformer fault diagnosis.
Volume: 17
Issue: 3
Page: 1780-1793
Publish at: 2026-09-01

FEA-based optimization of switches for reluctance motors

10.11591/ijpeds.v17.i3.pp1675-1687
Hiba Esam Aziz , Abdullah K. Shanshal , Imad Idan Abed Al-Khalaf , Tamer Kamel
Switched reluctance motors (SRMs) are considered one of the important machines used in industry sectors due to their simple structure, robustness, and high-speed capability. However, the performance is often limited by high torque ripple and acoustic noise due to the uneven magnetic flux distribution. This paper presents an optimization study for improving flux distribution by shape design modifications of the stator and rotor geometry using finite element analysis (FEA) combined with the whale optimization algorithm (WOA). More specifically, FEA is utilized to calculate the magnetic field behavior, torque characteristics, and core losses for different structural geometries of the proposed design, while WOA systematically searches for the optimum values of shape parameters. Thus, the simulation results show that the proposed approach significantly improves the uniformity of flux distribution, hence reducing torque ripple and improving the efficiency. The integration of FEA with the WOA provides a practical and effective way to achieve better SRM performance without further complication of control algorithms.
Volume: 17
Issue: 3
Page: 1675-1687
Publish at: 2026-09-01

Sliding-mode fuzzy logic controller based direct torque control and ripple minimization of induction motors

10.11591/ijpeds.v17.i3.pp1714-1727
S. Sujitha , Hima Bindu Eluri , P. B. Savitha , S. Venkateshwarlu
Conventional direct torque control is one of the most effective strategies for managing the torque of an induction machine (DTC), which is one of the most common types of machines. Instead, the DTC's inadequate control ability is made worse at low speeds by the audible flux and torque waves that are generated by its extremely low switching frequency. This is because the DTC's switching frequency is exceedingly low. In an effort to address these concerns, a variety of direct torque control strategies focused solely on flux and torque as their primary concerns. An improvement in DTC control and the elimination of ripples in induction motors are the goals of this research, which introduces a sliding mode fuzzy logic controller technique. The complexity of the algorithm, sensitivity of the parameters, the tracking speed, the switching loss, and the ripple reduction properties of the technique will all be thoroughly investigated. Simulation of the control mechanism is performed with MATLAB/Simulink in order to verify that it functions as intended.
Volume: 17
Issue: 3
Page: 1714-1727
Publish at: 2026-09-01
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