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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

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

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

Textile industry innovation: systematic review of key trends and particularities

10.11591/ijra.v15i3.pp720-736
Sebastián Cardona-Acevedo , Alejandro Arango-Correa , Diana Carolina Rios Echeverri , Alejandro Valencia-Arias , Jhon Edward Aguirre Cuervo
Innovation in the textile industry is a key strategic factor, influenced by geographic disparities, structural challenges, and rapid technological change. However, fragmented knowledge makes it difficult to fully understand the phenomenon. This study aimed to analyse how various types of innovation appear and interact in the global textile sector. A systematic literature review was carried out following PRISMA 2020 guidelines, using Scopus and Web of Science databases. From an initial pool of 94 articles, 19 met the inclusion criteria. Findings reveal that beyond specific advancements like automation or smart textiles, structural tensions hinder the integrated adoption of technological, organisational, and sustainability innovations. The diversity of analytical approaches shows there is no single, unified path to innovation in this sector. Instead, multiple innovation trajectories coexist, influenced by local conditions and unequal institutional capacities. In addition, knowledge gaps between developed and emerging regions, as well as the lack of focus on early stages of the supply chain, highlight the need to rethink research priorities. Ultimately, innovation in the textile industry must be understood as a comprehensive process that brings together technology, organisational change, and sustainability, requiring a holistic approach to improve competitiveness and ensure long-term transformation across the sector.
Volume: 15
Issue: 3
Page: 720-736
Publish at: 2026-09-01

Graph-guided contrastive transformer architecture for robust and explainable network intrusion detection

10.11591/ijra.v15i3.pp698-708
Archana Jayapal , Kamalakkannan Somasundaram , Arun Kumar Ramamoorthy
Intrusion detection systems (IDS) are very instrumental in protecting contemporary network infrastructures against the ever-advancing cyberattacks. Conventional signature-based and machine learning-enabled IDS solutions frequently have difficulty when it comes to high false-positive rates, inability to flexibly adapt to novel attacks, and the lack of support for complex traffic dynamics. New deep learning architectures have better detection properties, yet are limited by feature overlap, temporality, and lack of extensiveness to generalization in changing network conditions. To overcome these issues, this paper presents a new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility. The framework combines feature interaction by graph modeling, contrastive representation learning, and a sparse self-attention transformer to effectively learn global traffic relationships and behavioral variations. The CSE-CIC-IDS2018 data is used to test the proposed method in real network conditions.
Volume: 15
Issue: 3
Page: 698-708
Publish at: 2026-09-01

AI driven automated library assistance using pick-to-light system

10.11591/ijra.v15i3.pp639-646
Archana S. Ubale , Vaishali Baste , Harshada Bhushan Magar , Nilakshee R. Rajule
This paper presents the design and implementation of a library assistance system that uses a pick-to-light mechanism and integrates AI-based book recommendations. Book retrieval done manually in libraries is frequently time-consuming, prone to errors, and inefficient. As a solution, we will suggest a hybrid library assistance system, which would be based on an ATmega328 microcontroller, user identification by RFID, pick-to-light, and an AI-based collaborative filtering recommender. The user is directed to the preferred books through the LEDs placed on the shelves, and the AI module gives the user personalized suggestions. The experimental outcomes of 100 users show significant advances over the old traditional manual processes. The time spent searching for a book dropped by 96.4 seconds to 34.8 seconds, the error rate dropped to 3.2%, user satisfaction went up to 4.6, and the success rate of the entire book search process went up to 98.1%. The 500 retrieval cycles of the stress testing showed stability, reliability, and uniformity of the system, and the performance of the LED-RFID. These findings suggest that the proposed system is highly efficient for the library, minimizes human error, and positively impacts the user experience, making it applicable to real-life library environments.
Volume: 15
Issue: 3
Page: 639-646
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

Interpreting potato disease classification using explainable artificial intelligence

10.11591/ijaas.v15.i3.pp1123-1130
Rakesh Kumar Gumasta , Ajay Somkuwar
Timely and accurate crop diseases detection is most important for ensuring global food security. For detecting diseases in crops, many different machine learning (ML) models were proposed. These models work as a black-box, and without proper explanation of these models’ decisions, farmers may find it difficult to trust these systems. For this, many model explainability methods were also proposed. All these methods have been evaluated qualitatively, but their quantitative and cross-comparison are missing. This study addresses this gap by emphasizing quantitative validation of models’ explanations. This study investigates the interpretability and performance of two approaches for potato leaf disease classification: i) manual feature engineering with relief-based feature selection and artificial neural network (ANN) classification with local interpretable model-agnostic explanations (LIME) and ii) deep convolutional neural networks (CNNs) based on mobile network version 2 (MobileNetV2) with gradient-weighted class activation mapping (Grad-CAM). Quantitative assessment of explanation quality was performed using fidelity and robustness metrics. While LIME achieved a lower average fidelity drop 10.90% and higher robustness 84.27% compared to Grad-CAM 18.68% fidelity drops and 79.38% robustness, Grad-CAM provided clearer visual explanations. These findings suggest that explanation effectiveness is influenced by model design and data characteristics, highlighting the need for careful selection of interpretability techniques in precision agriculture applications.
Volume: 15
Issue: 3
Page: 1123-1130
Publish at: 2026-09-01

Social media resonance on destination dispersion to predict tourist behavior and travel distance using geospatial analysis

10.11591/ijaas.v15.i3.pp883-893
Nindyo Cahyo Kresnanto , Wika Harisa Putri , Muhamad Willdan
This study investigates the influence of social media popularity on tourist destination preferences in Gunungkidul Regency, Yogyakarta, Indonesia, using an integrated big data and geographic information system (GIS) approach. Field surveys involving 504 respondents were combined with sentiment analysis of 30,432 cleaned tweets collected from X (formerly Twitter). Spatial analysis was applied to identify tourist origin patterns and destination attractiveness. The results show that neutral sentiment dominates online discussions, indicating generally positive but moderate tourist experiences. Destinations with higher popularity on social media, particularly Heha Sky View, Drini Beach, and Indrayanti Beach, attracted visitors from broader and more diverse origins. Spatial analysis also indicates stronger preferences for coastal and highland destinations. The integration of big data and GIS enhances understanding of tourism behavior and spatial destination patterns. Future studies are recommended to integrate multiple social media platforms and comparative spatial analyses to improve tourism planning and destination management.
Volume: 15
Issue: 3
Page: 883-893
Publish at: 2026-09-01

Intelligent MPC for DFIG wind turbines

10.11591/ijpeds.v17.i3.pp2047-2057
Amira Lakhdara , Tahar Bahi , Amina Azizi , Amina Benabda
This paper presents and evaluates advanced control strategies to enhance power tracking and robustness in doubly fed induction generator systems operating under realistic and perturbed wind conditions. In addition to the conventional field-oriented control, we develop a model predictive control approach that determines the optimal rotor voltage vectors by minimizing a quadratic cost function, as well as a fuzzy-weighted model predictive control in which the cost weight is adjusted online based on the tracking error and its derivative. The dynamic models used accurately represent the key behaviors of the doubly fed induction generator and its rotor-side and grid-side converters. MATLAB/Simulink simulations are carried out using two wind scenarios: a smooth sinusoidal profile and a filtered stochastic profile, while a robustness test introduces variations in rotor parameters during operation. The results demonstrate that the fuzzy-weighted model predictive control achieves faster convergence, lower steady-state error, and improved robustness, all while maintaining reasonable converter effort and acceptable power quality.
Volume: 17
Issue: 3
Page: 2047-2057
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

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

Spatiotemporal digital twin for city-scale EV charging infrastructure using LSTM-GNN fusion and resilience-driven optimization

10.11591/ijpeds.v17.i3.pp2271-2280
Deepa Somasundaram , B. Ravisankar , Chavvakula Janaki Devi , Ajay Babu Bathula , Sabarimuthu Muthusamy , K. Vinoth
The rapid growth of electric vehicles (EVs) requires intelligent and resilient planning of charging infrastructure under dynamic urban conditions. This paper proposes a city-scale spatiotemporal digital twin (SDT) that integrates LSTM-GNN fusion with resilience-driven hybrid optimization (GA-PSO-deep reinforcement learning) for adaptive EV infrastructure management. The LSTM model captures temporal variations in charging demand, while the graph neural network (GNN) learns spatial dependencies across charging stations, mobility networks, and grid components. Unlike existing approaches, the proposed framework incorporates power electronics-aware modeling, including charger power conversion system (PCS) efficiency, switching losses, and harmonic distortion constraints, ensuring realistic grid interaction. The digital twin also considers energy system metrics such as transformer loading, voltage deviation, and renewable energy variability, along with EV drive-cycle characteristics like fast charging and battery limits. Simulation results show that the proposed model improves demand prediction accuracy by 14-22%, reduces grid overload probability by 35%, lowers operational cost by 18%, and achieves improved power quality performance compared to conventional methods. The system maintains a high resilience index (>0.92) under stress scenarios. Overall, this work presents a holistic AI-driven digital twin framework that enhances grid stability, supports sustainable EV integration, and enables scalable deployment of future smart charging infrastructure.
Volume: 17
Issue: 3
Page: 2271-2280
Publish at: 2026-09-01
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