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31,377 Article Results

Physics-informed reinforcement learning for adaptive high-frequency injection in encoderless low-voltage PMSM drives

10.11591/ijpeds.v17.i2.pp873-884
Surendar Aravindhan , Manoharan Kavitha , J. Karthika
It is difficult to control permanent magnet synchronous motor (PMSM) drives running at extra-low voltages with encoderless control because the back-EMF signal to estimate rotor position is weak, and this requires the injection of high-frequency (HF) signals. Traditional methods use constant or manually tuned injection levels, and these tend to cause large torque ripple, inaccurate estimation when under dynamic loading, and an inability to counteract parameter drift. The paper is related to the issue of online optimal HF injection amplitude choice in the encoderless 48 V PMSM drives and proposes a physics-inspired reinforcement learning (PIRL) system. This is aimed at obtaining the right low-speed positioning and reducing the torque ripple and power losses on different operating conditions. The suggested approach incorporates directly into the reinforcement learning reward terms the PMSM electromagnetic voltage equations, which restrict exploration to physically consistent space and enhance stability in the learning process. The PIRL agent is trained in a deep deterministic policy gradient architecture in a MATLAB/Simulink-Python co-simulation environment, based on which the PIRL agent adjusts the injection amplitude of HF in real time. Simulation outcomes show that the suggested methodology reaches approximately four times faster convergence with conventional reinforcement learning and reaches up to 65 percent of torque ripple reduction without a disturbed position estimation when operated in a speed range of 0-500 rpm. The findings show that physics-informed learning offers an efficient and energy-saving solution to adaptive encoderless control in extra-low-voltage PMSM drives, which has better resilience to changes in parameters with a low computational cost.
Volume: 17
Issue: 2
Page: 873-884
Publish at: 2026-06-01

Advanced IoT-integrated real-time fire detection and automated mitigation system

10.11591/ijict.v15i2.pp861-868
Rama Krishna Peddarapu , Ajimera Abhinav , Gnana Sathwika V. N. V. , Poosa Brijesh , Amrutha Varshini Ravula
In the field of industry and commerce safety, tackling the most challenging and ongoing fire threats requires the advance internet of things (IoT) integrated real-time fire detection and automated mitigation system. Leveraging IoT and multi-modal sensing in fire safety, the system combines flame, gas, and humidity sensors and cameras to provide continuous real time monitoring and appropriate management of the threats. Real-time automated hazard interventions, such as sprinkler system engagement and geocoded alerts to fire departments, significantly improve life safety outcomes of the system. Active damage mitigation IoT devices provide integrated damage mitigation safety and individual IoT device remote monitoring. In the scope of industry and commerce, this system is a demonstration of the impact of IoT on improving fire safety.
Volume: 15
Issue: 2
Page: 861-868
Publish at: 2026-06-01

Predicting battery life performance using artificial intelligence techniques in electric vehicles

10.11591/ijict.v15i2.pp805-812
Debani Prasad Mishra , Munavath Pavan Kalyan , Shivam Tyagi , Piyushjeet Piyushjeet , Shiv Grover , Surender Reddy Salkuti
Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Volume: 15
Issue: 2
Page: 805-812
Publish at: 2026-06-01

Exploring player interaction and team cooperation in MMOG playability enhancement

10.11591/ijict.v15i2.pp644-654
Gong Xiaoxue , Lili Nurliyana Abdullah , Azrul Hazri Jantan , Noris Mohd Norowi , Fatimah Sidi , Gulmira Abildinova
The massively multiplayer online games (MMOGs) continue to grow in popularity, and it has become particularly important to understand the key factors that influence team playability. While existing research has focused primarily on system functionality and individual player experience, insufficient attention has been paid to the role of team dynamics in player satisfaction. This study focuses on the core variables that influence team playability, including teamwork, task dependency, team loyalty (TLO), and team relationships (TR), and explores how these variables work together to influence player experience. This study used a combination of exploratory research (multi-variates) and a questionnaire survey (N=1064) to initially construct a team playability model, which was validated by structural equation modeling (SEM). The results show that TR have a significant positive effect on teamwork efficiency, and captains with transformational leadership (TL) styles not only enhance TR but also further improve overall team effectiveness (TE) and player satisfaction. This study provides MMOG developers with theoretical support for designing game mechanics centered on team interaction to enhance overall playability and player stickiness.
Volume: 15
Issue: 2
Page: 644-654
Publish at: 2026-06-01

Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks

10.11591/ijict.v15i2.pp623-633
Abhay Raghunath Gaidhani , Amol D. Potgantwar
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Volume: 15
Issue: 2
Page: 623-633
Publish at: 2026-06-01

Intelligent home automation framework using sensor fusion and machine learning for energy efficiency and thermal comfort

10.11591/ijict.v15i2.pp545-552
Franklin Ovuolelolo Okorodudu , Gracious Chukwuweike Omede , Etinosa Eugene Osawe
This paper presents an innovative, intelligent home automation framework integrating sensor fusion and machine learning to promote energy efficiency and thermal comfort in residential settings. Utilising low-cost hardware such as the Arduino Uno R3, passive Infrared (PIR) sensors, KY-018 photoresistors, and KY-028 temperature sensors, the system achieves a human presence detection accuracy of 95.3% via a random forest classifier. Over a three-month period, testing in several homes showed that the system is 99.7% reliable, responds in 1.2 seconds, and costs 85% less than commercial options. This research lays the groundwork for sustainable smart homes by providing a mathematical model for optimizing energy use and a unified modeling language (UML) model of the system architecture. These results show how important it is to have open-source technology that is cheap and could help smart building systems spread around the world. The study utilized a controlled experimental design featuring five families, with sensor data gathered at 10-second intervals over a three-month period. A random forest classifier trained on 10,000 labeled data points could correctly guess whether or not a person was present 94.8% of the time and 95.7% of the time. The framework is useful because it combines cheap sensors with a lightweight machine-learning pipeline that can work on small microcontrollers. This solves the long-standing problem of the cost performance gap seen in prior smart-home deployments.
Volume: 15
Issue: 2
Page: 545-552
Publish at: 2026-06-01

Enhancing Bitcoin price forecasting: a comparative analysis of advanced time series models with hyperparameter optimization

10.11591/ijict.v15i2.pp535-544
Amine Batsi , Mohamed Biniz , Rachid El Ayachi
This paper evaluates state-of-the-art time series forecasting to predict next day Bitcoin prices via distinct architectures and methodologies in a real-time setting. We study six advanced models, KAN, TimesNet, NBEATS, NHITS, PatchTST and BiTCN, applied to a Jan 1, 2023, to Dec 1, 2024. We simulate real world applications via a rolling forecast strategy, in which we predict daily prices from the most recent data. The dataset consists of daily Bitcoin closing prices and data preprocessing and integrity checks for its constituent data. Additionally, rigorous accuracy and reliability were investigated using performance metrics such as the MAE, RMSE, MAPE, and R². NBEATS and NHITS were the top performers, achieving an R² score of 0.967, explaining complex patterns in volatile cryptocurrency data. The specific importance of model architecture and further hyperparameter optimization in achieving higher forecasting accuracy is highlighted in this study. The practical implications of these findings for the advancement of time series forecasting in financial markets are leveraged here, where timely and accurate forecasts are critical.
Volume: 15
Issue: 2
Page: 535-544
Publish at: 2026-06-01

Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion

10.11591/ijict.v15i2.pp497-507
Smita Bharne , Ekta Sarda , Shamal Salunkhe
Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique enhanced transfer learning framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from magnetic resonance imaging (MRI) scans. The proposed model combine the strength of pre-trained convolutional neural networks (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.
Volume: 15
Issue: 2
Page: 497-507
Publish at: 2026-06-01

Semantic interoperability in IoT for Industry 4.0: Review, taxonomy, challenges, and future research

10.11591/ijict.v15i2.pp909-924
Devamekalai Nagasundaram , Erum Ashraf , Selvakumar Manickam , Shams Ul Arfeen Laghari , Shankar Karuppayah
Semantic interoperability is a critical enabler for achieving the Industry 4.0 vi sion, ensuring that heterogeneous IoT devices, systems, and applications can ex change and interpret data consistently. Despite its importance, achieving seman tic interoperability continues to pose significant challenges due to the diversity of data formats, standards, and ontologies used across industrial IoT environ ments. This paper presents a comprehensive review and taxonomy of semantic interoperability within Industry 4.0, analyzing existing frameworks, protocols, and ontological models. We classify current approaches based on their architec tural layers, semantic technologies, and application domains. Additionally, this study identifies the limitations of prevailing solutions, highlights open research challenges, and proposes future directions for enhancing semantic interoperabil ity in industrial IoT systems. The insights provided aim to support researchers and practitioners in developing scalable, secure, and semantically aligned IoT ecosystems for Industry 4.0.
Volume: 15
Issue: 2
Page: 909-924
Publish at: 2026-06-01

Multiclass classification using variational quantum circuit on benchmark dataset

10.11591/ijict.v15i2.pp578-587
Muhammad Hamid , Bashir Alam , Om Pal
Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.
Volume: 15
Issue: 2
Page: 578-587
Publish at: 2026-06-01

Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers

10.11591/ijict.v15i2.pp797-804
Debani Prasad Mishra , Sarita Samal , Manas Ranjan Sahu , Sonna Murari , Piyuskant Das , Surender Reddy Salkuti
This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Volume: 15
Issue: 2
Page: 797-804
Publish at: 2026-06-01

Trust-based secure routing in IoT networks using machine learning for enhanced anomaly detection and risk mitigation

10.11591/ijict.v15i2.pp839-849
Sangeetha Krishnaswamy , Arulanandam Karalagan
The rapid growth of the internet of things (IoT) has led to the development of new challenges in ensuring secure and reliable data transmission. This paper proposes a trust-based secure routing protocol (TBSRP) designed to mitigate security threats such as routing attacks in IoT networks. The core innovation lies in the dual-layer trust evaluation mechanism, which combines reputation-based trust and behavioral analysis to dynamically adjust routing decisions based on real-time performance and historical behavior of network nodes. To enhance security, the protocol incorporates an adaptive threshold mechanism that adjusts trust criteria based on observed network conditions and an anomaly detection system utilizing machine learning (ML) algorithms for real-time monitoring of node behavior. Experimental evaluation demonstrates that TBSRP outperforms existing protocols (such as Ad hoc on-demand distance vector (AODV), trust-based AODV (TB-AODV), energy-efficient secure routing (ESR), and Secure AODV (SEC-AODV)) in key performance metrics, including packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead. The proposed protocol exhibits strong resilience to the increasing number of malicious nodes and varying network conditions, making it highly effective for securing IoT networks. This work contributes to the development of adaptive, scalable, and secure routing protocols for IoT environments, with the potential for further optimization through advanced ML techniques and real-world implementation.
Volume: 15
Issue: 2
Page: 839-849
Publish at: 2026-06-01

Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis

10.11591/ijict.v15i2.pp769-777
Abhinandan Shirahatti , Ramesh Medar , Vijay Rajpurohit , Sanjeev Kaulgud , Mrutyunjaya Mathad Shivamurthaiah
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Volume: 15
Issue: 2
Page: 769-777
Publish at: 2026-06-01

Adaptive proportional integral control using neural networks for secondary frequency regulation in microgrids

10.11591/ijai.v15.i3.pp2229-2237
Belkasem Imodane , Mohamed Benydir , Sana Mouslim , Abdellah El Idrissi , Mohamed Ajaamoum , Brahim Bouachrine
Microgrids with high renewable energy integration face a challenge in maintaining frequency stability due to the reduced inertia of inverter-based generation and the intermittent nature of these sources. Although primary frequency regulation using virtual synchronous generator (VSG) strategies can provide fast support, it cannot fully bring the system frequency back to its nominal value. This limitation highlights the importance of secondary frequency regulation, which is implemented using proportional integral (PI) controllers. However, fixed parameter PI regulators often fail to adapt effectively to varying loads and fluctuating renewable generation. This paper proposes an adaptive secondary control strategy for microgrids that combines offline optimization with real time learning. Grey wolf optimization (GWO) is first applied offline to determine the optimal PI gains for multiple disturbance scenarios. These datasets are then used to train an artificial neural network (ANN), which updates the PI parameters in real time to achieve adaptive performance. The proposed control is implemented in a hybrid microgrid with a diesel generator, a permanent magnet synchronous generator (PMSG) wind turbine for primary support and a fuel cell for secondary regulation. Simulation results show that the adaptive PI controller improves frequency recovery and reduces steady-state error compared to conventional fixed gain PI.
Volume: 15
Issue: 3
Page: 2229-2237
Publish at: 2026-06-01

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

10.11591/ijece.v16i3.pp1188-1198
Hanae Mahmoudi , Hiba Ramadan , Jamal Riffi , Hamid Tairi
Multimodal registration of 3D medical images (3D-MReg) plays a key role in several medical applications and remains a very challenging task as it deals with multimodal images and volumetric objects at the same time. Recently, convolutional neural networks (CNNs) based approaches have been proposed to solve 3D-MReg. However, these techniques cannot preserve the global spatial context required for accurate affine registration since they rely on convolution and regional clustering operations. To solve these problems, we propose a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF). In a first step, our method investigates the power of ViT to capture global voxels dependencies for initial rigid alignment. Then we exploit the force of CNNs to focus on local details within pre-aligned concatenated input 3D moving and fixed images and estimate DDF, which is then applied to the moving labels. Our method has been validated in a prostate magnetic resonance imaging/transrectal ultrasound (MRI/TRUS) dataset and achieved promising results compared to previous work based on only CNNs.
Volume: 16
Issue: 3
Page: 1188-1198
Publish at: 2026-06-01
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