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Seamless UAV integration: a framework for network slicing as a service in 5G and beyond

10.11591/ijeecs.v43.i3.pp749-761
Bouzid Tarek , Noureddine Chaib , Mohamed Lahcen Bensaad
Unmanned aerial vehicles (UAVs) have demonstrated remarkable versatility across monitoring, delivery, and data collection applications. However, the growing complexity of network architectures with 5G and Beyond 5G (B5G) necessitates innovative solutions like network slicing, which faces challenges in radio access network (RAN) efficiency and optimization under higher frequencies. This paper introduces a novel framework for seamless UAV-assisted net work slicing as a service. By integrating UAVs directly into the network infrastructure, the framework enables on-demand delivery of tailored network slices to end users, addressing the limitations of traditional terrestrial approaches while enhancing efficiency and scalability. The proposed framework allows service providers and network administrators to deliver slices more effectively, while specifically tackling high-frequency RAN challenges by deploying UAVs as close-proximity relays to users. A key contribution is complete compatibility and interoperability with existing cellular infrastructure, ensuring seamless integration. The paper presents critical foundational concepts and a literature review, followed by the proposed framework’s architecture, layers, and methodology. Comprehensive tests and comparisons evaluate the framework’s performance, concluding with a discussion of applications and potential enhancements.
Volume: 43
Issue: 3
Page: 749-761
Publish at: 2026-09-01

Automated recognition of Thai fabric patterns using transfer learning with ResNet-50

10.11591/ijeecs.v43.i3.pp847-856
Kittiya Poonsilp , Pijitra Jomsri , Dulyawit Prangchumpol , Thammarat Panityakul
Traditional Thai fabric patterns are valuable cultural heritage, but expert knowledge of these patterns is declining. This study uses a convolutional neural network (CNN) with transfer learning (ResNet-50) to classify traditional Thai fabric patterns automatically. We collected 961 images across 19 pattern categories, split into training (57%), validation (11%), and test (32%) sets. Using ImageNet pre-trained weights and progressive fine-tuning, the model achieved 99.68% test accuracy with macro-averaged F1-score of 99.35%. Ablation studies validated our approach: augmentation improved accuracy by 2.26%, fine-tuning outperformed frozen backbone by 4.84%, and backbone comparison showed ResNet-50 achieves higher F1-score than MobileNetV2 while MobileNetV2 offers 90% parameter reduction for mobile deployment. Controlled stress tests demonstrated robustness under image degradations typical of smartphone photography. Unlike previous studies using controlled conditions, our dataset includes real-world variations. These results show that transfer learning with small datasets can match expert-level pattern recognition for cultural heritage preservation.
Volume: 43
Issue: 3
Page: 847-856
Publish at: 2026-09-01

Low-cost BLDC drive with PBBO-tuned FOPID controller for enhanced speed regulation and torque ripple reduction

10.11591/ijeecs.v43.i3.pp956-964
Pandi Maharajan M. , Rohini G. , Ravindran Ramkumar , Dharani Kumar Narne
Torque ripple (TR) minimization in brushless DC (BLDC) motors has become a critical research focus due to its direct impact on drive performance, efficiency, and reliability. Conventional BLDC drives typically employ large DC-link capacitors, which increase system cost and weight and are highly sensitive to operating temperature, thereby reducing lifetime. To address these limitations, this work proposes a low-cost capacitor-based BLDC drive integrated with a torque ripple compensation (TRC) technique and optimized control using the probabilistic biogeography-based optimization (PBBO) algorithm. The PBBO method is employed to tune the parameters of a fractional-order proportional-integral-derivative (FOPID) controller, ensuring effective speed regulation and TR reduction. By probabilistically refining migration and emigration rates, PBBO enhances convergence and eliminates redundant species movements, leading to superior controller parameter optimization. Simulation studies validate the proposed PBBO-FOPID approach, demonstrating significant improvements in TR reduction and speed control compared to conventional controllers such as DGOA-FOPID and spider web-based controller (SWC). Results confirm that the PBBO-FOPID controller achieves smoother torque response, reduced ripple, and enhanced speed regulation, establishing it as a cost effective and high-performance solution for BLDC motor drives.
Volume: 43
Issue: 3
Page: 956-964
Publish at: 2026-09-01

Automated smart handbag with enhanced women's safety using cutting edge technology

10.11591/ijra.v15i3.pp669-677
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ashish Ragavendra Nattamai Uthayakumar , Arulmurugan Nagarajan Renukadevi , Rishikeshwaran Balamurugan Rani , Rupa Kesavan
Women’s safety has been an area of concern, especially in public places where timely assistance cannot be provided. To mitigate this problem, this paper proposes an intelligent handbag-based women’s safety system that utilizes the concept of biometric identification, location tracking, and edge computing-based AI threat verification. The proposed system, unlike other traditional women’s safety devices that rely on GPS-GSM for emergency alerts and are more likely to send false alarms, utilizes fingerprint identification for secure and authorized use, along with YOLO v3 vision model on an ESP32-CAM for threat verification. Upon failure in the authentication process or threat detection, the system sends an SOS message with the current location via GSM with the help of GPS coordinates. The system achieves an emergency response time of 33 seconds, primarily limited by GPS acquisition delay. The results confirm the effectiveness and applicability of the proposed system in providing an intelligent emergency response system for women’s safety.
Volume: 15
Issue: 3
Page: 669-677
Publish at: 2026-09-01

URL-based phishing detection using XGBoost with engineered features

10.11591/ijeecs.v43.i3.pp908-927
Jawaher Alharbi , Manal Bayousef , Hind Almisbahi
URL-based phishing involves fake uniform resource locators (URLs) created by attackers to trick users into believing they are visiting a legitimate website and thereby steal their confidential information. While several powerful machine learning (ML) and deep learning (DL) studies exist to detect phishing, they still face limitations. Many studies rely on third-party intervention to extract features, which introduces delays that make them unsuitable for fast detection. Another limitation is that existing studies often use small datasets, and traditional features hinder models' ability to learn new phishing techniques, resulting in poor generalization. Therefore, developing new features is crucial to ensure that anti-phishing tools can keep pace with evolving phishing tactics. In addition, the existing studies do not report detection time, which is important for fast detection, and reduces methodological clarity. This paper aims to address these limitations by applying a neural network model and traditional ML classification algorithms to support browser-based phishing detection that balances high accuracy with fast detection. Our XGBoost model achieved 98% accuracy on the test set, utilizing 40 third-party-independent features. Additionally, we achieved an average response time of 0.026225 seconds and an average computation time of 1.6977×10⁻6 seconds per URL, which demonstrates competitive speed. We provided a table of features from recent studies, together with their documented sources, to support future research and analyze key URL-based features.
Volume: 43
Issue: 3
Page: 908-927
Publish at: 2026-09-01

ROVAA: Offline attendance automation using a voice–OCR-based 3-DOF robotic arm with Raspberry Pi

10.11591/ijra.v15i3.pp561-576
Rajanikanth Kashi Nagaraj , Archana Harihara Ranganatha , Surendra Hanumanthaiah Honnamachanahalli , Venu Manighatta Gopalakrishnappa
Conventional classroom attendance systems suffer from limitations in accuracy, hygiene, data privacy, and reliability in low-connectivity environments, whether they are manual, cloud-dependent, or single-modality systems. To address these gaps, this paper presents ROVAA, a low-cost, fully offline, AI-driven robotic attendance system in the classroom environment that uniquely integrates three complementary modalities: offline voice recognition, optical character recognition (OCR), and a 3- degrees of freedom (DOF) robotic arm controlled via inverse kinematics, an integration not demonstrated in prior work. The system operates on a Raspberry Pi 4 model B and employs the Vosk speech recognition model and Tesseract OCR for accurate offline processing. Audio and visual inputs are matched in real time to enable the arm to mark attendance at pre-calibrated positions on a touchscreen. Experimental validation under varied lighting and acoustic conditions yielded 96.2% speech recognition accuracy, 95.8% OCR accuracy, and 97.6% robotic arm precision, producing an overall system success rate of 92.8%, demonstrating that high reliability is achievable without cloud infrastructure. The system is designed for cost-effectiveness, data privacy, and scalability, making it suitable for resource-constrained environments such as rural schools and institutions with limited network access. It additionally serves as an educational platform for human–robot collaboration.
Volume: 15
Issue: 3
Page: 561-576
Publish at: 2026-09-01

Optimal robust control for self-balancing robot-based feedback linearization and Atom Search Optimization

10.11591/ijra.v15i3.pp519-528
Alaa Jumaah Al-Maiahy , Yahya Ghufran Khidhir , Adnan Jabbar Attiya , Hisham H. Jasim
In this paper, a robust control method is suggested for attitude control of the two-wheeled self-balancing robot by combining feedback linearization with sliding mode control techniques. The proposed method takes into account important challenges such as external disturbance and system uncertainty. Feedback linearization cancels the nonlinearities in the dynamics of the robotic system, while the sliding mode control handles the uncertainties and the external disturbance. The parameters of the proposed controller are selected by tuning the controller with the Atom Search Optimization algorithm. MATLAB is used to simulate the proposed controller. Simulation results indicate a good performance of the presented controller with high robustness compared with the proportional-integral-derivative (PID) controller. Moreover, the proposed method reduces the rise time by approximately 40% and 50% with respect to PID. These results illustrate the feasibility of the presented control method to be used for real-time implementation in autonomous robotic balancing systems.
Volume: 15
Issue: 3
Page: 519-528
Publish at: 2026-09-01

Hybrid AC/DC and conventional AC house efficiency for net zero energy homes

10.11591/ijape.v15.i3.pp1458-1474
Taufik Taufik , Heru Nurwarsito , Tyler Bury , Rahman Azis Prasojo
The transition toward net-zero energy homes (NZEH) requires residential electrical systems that can efficiently integrate renewable generation, battery storage, and both AC and direct current (DC) loads. Although DC and hybrid AC/DC residential systems have been widely studied, limited work directly compares hybrid AC/DC and conventional AC house architectures under different grid standards, power levels, and AC/DC load ratios while considering DC bus losses. This study presents a MATLAB/Simulink-based steady-state efficiency comparison between hybrid AC/DC and conventional AC residential electrical systems. Twelve models were developed, consisting of six hybrid AC/DC and six conventional AC configurations under 120 V/60 Hz and 230 V/50 Hz standards. The models include PV generation, battery storage, inverter, AC/DC converter, multiple-input single-output (MISO) converter, and line-resistance effects. Results show that hybrid AC/DC houses achieve 4-11% higher efficiency than conventional AC houses when DC load demand remains below approximately 1.5-2.0 kW, mainly due to reduced conversion stages. At higher DC load levels, the efficiency advantage decreases because of copper losses in the 48 V DC bus. Increasing the DC bus voltage to 60 V reduces current-related losses and extends the efficient operating range. These findings indicate that hybrid AC/DC distribution is most suitable for residential applications with low-to-moderate DC demand, such as lighting, electronics, communication devices, and other DC-compatible appliances. The main contribution of this study is identifying the operating range, efficiency limit, and practical design implications of hybrid AC/DC residential distribution for future NZEH applications.
Volume: 15
Issue: 3
Page: 1458-1474
Publish at: 2026-09-01

Performance evaluation of a GA-tuned PID controller for a buck-boost converter based on integral error metrics

10.11591/ijape.v15.i3.pp1168-1179
Mahabaleshwara Bhat P. , Subramanya Bhat
DC-DC buck-boost converters are widely used in photovoltaic (PV) systems to maintain a regulated output voltage under varying source and load conditions. Although genetic algorithms (GA) based proportional-integral-derivative (PID) controllers are commonly used, the influence of different integral error performance indices on controller behavior has not been systematically examined. This paper presents a detailed study of GA-based PID tuning for a buck-boost converter operating in both buck and boost modes. Four integral error metrics-integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE)-are employed as fitness functions to obtain optimal controller gains. A detailed MATLAB/Simulink model of the converter working in continuous conduction mode (CCM) is developed to evaluate controller performance under step input, source transients, and load transients. The results demonstrate that the selected error metric significantly affects transient characteristics, including rise time, settling time, overshoot, and steady-state error. Controllers tuned using ITAE and ITSE provide better transient performance compared to IAE and ISE based tuning. The results highlight the critical role of objective function selection in GA based PID optimization and provide practical guidelines for buck-boost converters in PV applications.
Volume: 15
Issue: 3
Page: 1168-1179
Publish at: 2026-09-01

Sustainable e-mobility with controlled charging scheme based on grid energy using machine learning

10.11591/ijape.v15.i3.pp1036-1050
Archana Kadam , Ramesh Mali , Reena Gunjan , Virendra Shete , Pradeep Mane
The electric vehicle (EV) popularity has taken off among consumers, which has in turn led to efforts to create an efficient EV charging infrastructure. This paper addresses this challenge by proposing a scheduled charging scheme that uses real-time data from a grid-connected charging station at Baner, Pune, operated by Pune Mahanagar Parivahan Mahamandal Ltd (PMPML). The proposed system makes use of advanced machine learning techniques such as the Stochastic dual coordinate ascent (SDCA) and Fast Forest (FF) algorithm, both of which allow for precise and efficient computations to predict charging finish times and make optimal scheduling decisions. The use of these algorithms in conjunction with ToU tariffs is cost effective when compared to flat rate tariffs. Grid load analysis shows that scheduling according to time lowers peak demand, equalizes load distribution, and lowers operating costs. A quantitative comparison has demonstrated both grid stability and economic efficiency gains over uncontrolled charging. The result is an extremely flexible framework for different charging events or stations which will be a viable way of managing energy in the fast-growing EV charging networks.
Volume: 15
Issue: 3
Page: 1036-1050
Publish at: 2026-09-01

Performance evaluation of rooftop photovoltaic integration using self-consumption and self-sufficiency indicators with IoT-based monitoring

10.11591/ijape.v15.i3.pp1399-1408
Syukron Al-Fajri , Syafii Syafii
Indonesia’s energy transition has increased the importance of rooftop photovoltaic (PV) systems, yet their integration with the PLN grid remains challenging because intermittent generation affects supply-demand balance and complicates the evaluation of local PV utilization. This study proposes and evaluates a low-cost internet of things (IoT)-based AC-side monitoring system for residential rooftop PV integration using an ESP32 microcontroller and PZEM-004T energy sensors. The proposed framework monitors three measurement points, namely PV output, grid line, and household load, and classifies the operating condition into export and import states. Based on measured historical field data, the operational performance of the system is assessed using the self-consumption ratio (SCR) and self-sufficiency ratio (SSR). The results show that PV generation dominates during daytime operation, whereas household demand becomes increasingly dependent on the PLN grid from late afternoon to night. Across the observation period, daily SCR ranges from 1.08% to 6.86%, while daily SSR ranges from 3.07% to 74.61%, indicating limited overall self-consumption but relatively strong daytime load support. These findings show that the proposed low-cost AC-side IoT monitoring framework can effectively quantify rooftop PV-grid interaction and provide a practical basis for evaluating local PV utilization and grid dependence under actual household operating conditions.
Volume: 15
Issue: 3
Page: 1399-1408
Publish at: 2026-09-01

Cost-effective hardware solutions for experimental validation in renewable energy emulation and storage systems

10.11591/ijape.v15.i3.pp1287-1298
Yassine El Asri , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Marouane El Ancary , Hafsa Abbade , Mohammed Chiheb , Mohamed Koundi
Experimental validation is essential in renewable energy emulators and energy storage systems to ensure reliability, accuracy, and practical implementation. However, the high cost of AC/DC converters, measurement circuits, and microcontroller-based control platforms limits access to experimental validation, particularly in developing regions. This paper proposes cost-effective hardware and software solutions for validating renewable energy systems and storage management. The proposed approach is based on affordable power converters, low-cost voltage and current measurement circuits, data acquisition tools, and open-source control platforms. The methodology includes the design, implementation, and experimental testing of these low-cost solutions under different operating conditions. The obtained results demonstrate satisfactory measurement accuracy, stable behavior, and acceptable error margins, confirming the feasibility of the proposed setup for experimental validation. These solutions provide a scalable and accessible alternative to expensive laboratory platforms, enabling researchers and institutions with limited resources to perform reliable experimental studies and contribute to the advancement of sustainable energy technologies.
Volume: 15
Issue: 3
Page: 1287-1298
Publish at: 2026-09-01

Effect of power and voltage variations on transformer core losses and geometry for two distinct core materials

10.11591/ijape.v15.i3.pp995-1008
Kamran Dawood , Furkan Gezer , Güven Kömürgöz Kırış , Semih Tursun
This study presents a comprehensive comparison of transformer core size and no-load losses, focusing on transformers with power ratings ranging from 400 kVA to 3200 kVA. The research evaluates transformer performance at three distinct primary voltage levels: 6 kV, 15 kV, and 33 kV, while maintaining a constant secondary voltage of 0.4 kV. Additionally, the study investigates the impact of two commonly used transformer core materials, M4 and H0, on core design, with a particular focus on their effects on no-load losses. Another central aspect of the analysis is the examination of core geometry, including the cross-sectional area and height of the transformer core, across various power ratings and voltage levels. The results reveal a clear relationship between core size and energy efficiency. M4 cores exhibit higher no-load losses compared to H0 cores; additionally, H0 cores demonstrate better overall efficiency, making them ideal for high-efficiency applications. The findings underscore the importance of selecting the right core materials and geometries to balance performance, efficiency, and size. These insights are intended to guide researchers and transformer designers in making informed decisions when developing energy-efficient solutions for diverse applications across the electrical power industry.
Volume: 15
Issue: 3
Page: 995-1008
Publish at: 2026-09-01

A deep learning approach for electric vehicle battery charging duration estimation using IoT

10.11591/ijape.v15.i3.pp1375-1385
Tumuluri Kanthimathi , Adhimoolam Sairam , Durairaj Chandrakala , Moorthy Radhika , Bichagal Shadaksharappa , Pitchai John Britto , Minakshi Sanadhya , Balasubramanian Suganya , Chelliah Srinivasan
The rapid development of electric vehicles (EVs) has increased the desire for precise charging duration estimate to enhance charging station administration and elevate customer experience. This research presents a deep learning (DL) architecture that uses internet of things (IoT)-enabled data to categorise charging duration into short, medium, and long classifications. The assessment dataset comprises many numerical and categorical variables affecting battery performance and charging behaviour, providing a thorough foundation for prediction. The system utilises a deep neural network (DNN) architecture with nonlinear transformations and regularisation techniques, trained with adaptive optimisation to provide durable convergence. Extensive experiments indicate that the proposed model achieves an overall accuracy of 99.26%, markedly improving traditional machine learning (ML) techniques. These results highlight the potential of DL to adeptly discern complex linkages in charging dynamics, providing dependable predictions for duration classification. The framework provides an advanced basis for implementation in smart charging infrastructures, facilitating effective scheduling, minimizing waiting times, and endorsing predictive maintenance measures. It enhances the reliability and efficiency of EV charging ecosystems with data-driven, IoT-enabled DL technologies.
Volume: 15
Issue: 3
Page: 1375-1385
Publish at: 2026-09-01

Environmental footprint assessment of lithium-ion (Li-ion) batteries in electric scooters: a case study of PT Motor Listrik Indonesia

10.11591/ijape.v15.i3.pp955-964
Rafi Juniar Saputra , Aufar Fikri Dimyati , Silvi Istiqomah , Dwi Heru Siswantoro
Indonesia ranks as the world's sixth-largest contributor to CO2 emissions, with motorcycles alone responsible for 56,788 tons of CO2 in 2020. While electric motorbikes are considered a cleaner alternative due to their lack of exhaust emissions, their overall environmental impact must be evaluated throughout the entire battery life cycle. This study conducts a gate-to-grave life cycle assessment (LCA) on the lithium-ion (Li-ion) batteries used in MOLINDO electric motorbikes, covering the supplier, production, usage, and disposal stages. The total environmental impact was found to be 920 points (pt), with the usage phase being the largest contributor at 550 pt, followed by production at 185.5 pt, supplier at 179 pt, and disposal at 5.54 pt. Two improvement strategies were explored: reusing external battery cases, which slightly reduced the impact to 918.86 pt, and repurposing second-life batteries, which extended battery use but increased the impact to 998.04 pt due to additional energy requirements. Compared to conventional motorbikes, electric motorbikes still offer a major environmental advantage in the usage phase (550 pt vs. 3,397 pt). These findings suggest that, alongside the shift to electric mobility, targeted actions such as better battery reuse, more efficient recycling, and cleaner electricity sources are essential to fully unlock the environmental benefits of electric motorbikes.
Volume: 15
Issue: 3
Page: 955-964
Publish at: 2026-09-01
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