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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

Manufacturing mycelium moulds under controlled conditions using IoT

10.11591/ijict.v15i2.pp880-890
Subbulakshmi V. , Jeevaa Katiravan , Parvathy M. , Sridevi S.
In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.
Volume: 15
Issue: 2
Page: 880-890
Publish at: 2026-06-01

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
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

Smart water distribution for smart cities based on Internet of Things

10.11591/ijece.v16i3.pp1655-1668
Amal Douli , Khelifa Benahmed , Belkacem Draoui
Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.
Volume: 16
Issue: 3
Page: 1655-1668
Publish at: 2026-06-01

An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places

10.11591/ijece.v16i3.pp1261-1271
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.
Volume: 16
Issue: 3
Page: 1261-1271
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

Using the technology theory to adoption virtual reality among university students

10.11591/ijece.v16i3.pp1485-1492
Ghaliya AlFarsi , Raghad M. Tawafak , Roy Mathew , Sohail Iqbal Malik , Abir AlSideiri
Virtual reality is a technology field that has become an integral part in most areas of life. Before the 20th century, virtual reality consisted primarily of artificial illusions. Students encounter early obstacles in learning and the current virtual reality (VR) learning mechanism. The research is based on previous studies by filling in the blank by observing the problems that students were facing. The second main point of this research was unified theory using model of technology acceptance and use. This paper focuses on the adoption of a virtual reality learning model in order to improve student academic performance. The results of this paper prove that hypotheses have a positive impact on the factors to use the proposed model.
Volume: 16
Issue: 3
Page: 1485-1492
Publish at: 2026-06-01

Integrating BERT fine-tuning and genetic algorithm for superior depression detection in social media

10.11591/ijece.v16i3.pp1474-1484
Abd Allah Aouragh , Mohamed Bahaj , Fouad Toufik
Early detection of depression is crucial for minimizing its adverse effects on mental and physical health. Recent advancements in natural language processing facilitate the large-scale analysis of social media texts to identify depressive tendencies. Our study introduces a novel approach by integrating a genetic algorithm for hyperparameter tuning, optimizing the classification performance beyond conventional methods. We provide a comprehensive comparison of vectorization techniques, including term frequency-inverse document frequency (TF-IDF), Word2Vec, and a fine-tuned bidirectional encoder representation from transformers (BERT) model specifically adapted to our dataset. Using a dataset of 7,731 entries, we implemented standard pre-processing steps such as stop word removal and lemmatization before vectorizing the text. Five machine learning algorithms—decision tree, logistic regression, random forest, gradient boosting, and support vector machine—were evaluated, with hyperparameter tuning performed using a genetic algorithm. The highest accuracy (95.99%) and F1-score (95.91%) were achieved with the combination of fine-tuned BERT, support vector machine, and genetic algorithm optimization. This study demonstrates the advantages of integrating BERT fine-tuning with genetic optimization, outperforming traditional TF-IDF and Word2Vec approaches in depression detection.
Volume: 16
Issue: 3
Page: 1474-1484
Publish at: 2026-06-01

Sepsis detection using biomarkers and machine learning

10.11591/ijece.v16i3.pp1286-1297
Tuan Anh Vu , Dang Hoai Bac , Minh Tuan Nguyen
Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Volume: 16
Issue: 3
Page: 1286-1297
Publish at: 2026-06-01

A critical review of information retrieval techniques: current trends and challenges

10.11591/ijict.v15i2.pp456-464
Sanket D. Patil , Zahir Aalam
The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
Volume: 15
Issue: 2
Page: 456-464
Publish at: 2026-06-01

Mobile device application design for ThingSpeak interface using flutter

10.11591/ijict.v15i2.pp850-860
Moehammad Sauqy Ihza Zuliandra , Tigor Hamonangan Nasution , Ainul Hizriadi
The rapid development of internet of things (IoT) is prompting many people to design applications, particularly for monitoring applications based on mobile apps. This includes research designs to monitor electrical parameters from PV and the development of health monitoring applications. Previous research required a separate application to scan each IoT device. In this research, a mobile app-based IoT monitoring system was built using flutter. With this, people no longer need to design separate mobile apps for various IoT devices. This application utilizes the flutter framework, while the cloud component uses ThingSpeak. These research results show that data from multiple IoT devices can be transferred to the user’s mobile app. This application enables the monitoring of various IoT devices through a single mobile app, thereby enhancing the efficiency of IoT device design and management.
Volume: 15
Issue: 2
Page: 850-860
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

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

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
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