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31,456 Article Results

A hybrid adaptive neuro-fuzzy inference system and reptile search algorithm model for wind power forecasting

10.11591/ijece.v15i3.pp2857-2873
Mohamad I. Al-Widyan , Laith Abualigah , Ghaith M. Jaradat , Mutasem Khalil Alsmadi
Estimating the number of wind ranches generated in the upcoming minutes, hours, or days is the focus of wind power forecasting. Deep learning has garnered a lot of interest in wind control estimation because of how well they perform classification, grouping, and recurrence. The adaptive neuro-fuzzy inference system was successfully applied in wind power forecasting. However, its performance relies on optimal selection of hyperparameters. This study introduces a novel predictive model by incorporating the reptile search algorithm with adaptive neuro-fuzzy inference system (ANFIS) for short-term wind power forecasting. It employs reptile search algorithm (RSA), known for adjustable parameters, disentangled search, and consistent outcomes, to optimize ANFIS’s hyperparameters. Additionally, via exploitation during training, RSA performs a selection of best features in the dataset that contributes to the classification accuracy of ANFIS. This aims to enhance precision of the anticipated yield. Employing authentic wind power data from Jordan is undertaken to evaluate efficiency. The performance is compared with alternative techniques, including artificial neural networks, random forests, and support vector machines. Findings showed that ANFIS-RSA performs competitively for the well-known Chinese benchmark dataset (99.9% accuracy; 0.99 R2; 10.54 MAE; 11.62 RMSE) and is more robustly accurate than others over the Jordanian dataset (0.84.6% accuracy; 0.96 R2; 0.098 MAE; 0.203 RMSE).
Volume: 15
Issue: 3
Page: 2857-2873
Publish at: 2025-06-01

Investigation of the satellite internet of things and reinforcement learning via complex software defined network modeling

10.11591/ijece.v15i3.pp3506-3518
Arun Kumar , Sumit Chakravarty , Aziz Nanthaamornphong
The satellite internet of things (SIoT) has emerged as a transformative technology, enabling global connectivity and extending IoT infrastructure to remote and underserved regions. This paper explores the integration of SIoT with advanced reinforcement learning (RL) techniques through sophisticated software-defined networking (SDN) modeling. The study emphasizes SDN’s capability to offer flexible, dynamic, and efficient management of satellite-based IoT networks, addressing unique challenges such as high latency, limited bandwidth, and frequent mobility. To address these challenges, we propose an RL based approach for optimizing network resource allocation, routing, and communication strategies. The RL algorithm enables autonomous adaptation to real-time network conditions, tackling critical concerns such as spectrum management, energy efficiency, and load balancing, ensuring reliable connectivity while minimizing congestion and power consumption. Furthermore, SDN facilitates network programmability, enabling centralized control and streamlined management of SIoT systems. The proposed RL-driven SDN model is validated through simulation experiments, demonstrating significant improvements in throughput, network efficiency, and quality of service (QoS) metrics compared to traditional network models. This work advances the development of satellite IoT networks by providing a robust, scalable framework that integrates RL and SDN technologies, offering intelligent and efficient connectivity solutions to meet the growing demands of next-generation SIoT systems.
Volume: 15
Issue: 3
Page: 3506-3518
Publish at: 2025-06-01

Hybrid semantic model based on machine learning for sentiment classification of consumer reviews

10.11591/ijai.v14.i3.pp2001-2011
Palaniraj Rajidurai Parvathy , Nagarajan Mohankumar , Rajendran Shobiga , Gour Sundar Mitra Thakur , Mamatha Bandaru , Velusamy Sujatha , Shanmugam Sujatha
Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Volume: 14
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Depression and post traumatic stress disorder analysis with multi-modal data

10.11591/ijai.v14.i3.pp2358-2366
Rajalakshmi Sivanaiah , Angel Deborah Suseelan , Krupa Elizabeth Thannickal , Sanmati Pirabahar
With an increasing global population and more people living to the age when major depressive disorder (MDD) or post traumatic stress disorder (PTSD) commonly occurs, the number of those who suffer from such disorders is rising. Studies have also shown a high likelihood of comorbidity between these 2 disorders. This comorbidity can worsen symptoms, increase the risk of chronicity, and complicate treatment, significantly impacting patients’ emotional wellbeing and social and occupational functioning. There is a need to enable faster and reliable diagnosis methods, while taking into account the subjectivity of individuals and the role of behavioural cues. The proposed approach analyses the combination of audio, video and text input features (multi-modal data) of the subject to determine the severity class of MDD and PTSD. The DistilBERT transformer is used for learning and building a model with the textual modality and random forest classifiers for the audio and video modalities. An ensemble of these 3 models from 3 modalities performs better in the final classification of MDD and PTSD when compared to individual models. This work also covers a comparison of the models with different splits on the dataset. This ensembled system shows an improved accuracy of 2% to 7% for the MDD and PTSD multi class classification over the models tested on individual modalities.
Volume: 14
Issue: 3
Page: 2358-2366
Publish at: 2025-06-01

Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks

10.11591/ijece.v15i3.pp3494-3505
Mouad Choukhairi , Sara Tahiri , Ouail Choukhairi , Youssef Fakhri , Mohamed Amnai
The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Volume: 15
Issue: 3
Page: 3494-3505
Publish at: 2025-06-01

Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems

10.11591/ijece.v15i3.pp3267-3278
Ema Utami , Erwin Syahrudin , Anggit Dwi Hartanto
Blindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies.
Volume: 15
Issue: 3
Page: 3267-3278
Publish at: 2025-06-01

Techniques of Quran reciters recognition: a review

10.11591/ijai.v14.i3.pp1683-1695
Ibrahim Alomari , Asma Alshargabi , Mohammed Hadwan
The Quran is the holy book of the Islam. Reading and listening to the Quran is an important part of the daily life of Muslims. Muslims are keen to listen to recitations of Quran by skilled reciters to learn the correct recitation for the purpose of understanding and contemplating. Therefore, there are large variety of audio recitations for many skilled reciters. With the availability of this huge amount of recitations and also with the great progress in voice recognition technologies, many research efforts have been devoted to contribute making recitation better using artificial intelligence. One useful application in this area is identifying the reciters of the Quran. There are various solutions introduced by researchers; however, these solutions vary significantly in terms of accuracy, and efficiency. This research seeks to provide a review of these solutions. It also reviews available datasets using different criteria. Finally, some open issues and challenges were addressed.
Volume: 14
Issue: 3
Page: 1683-1695
Publish at: 2025-06-01

MOERID: enhancing open educational resource discoverability through an artificial intelligence-powered chatbot recommender system

10.11591/ijece.v15i3.pp3107-3117
Sandoussi Rima , Hnida Meriem , Daoudi Najima , Ajhoun Rachida
Open educational resources (OER) are valuable assets in learning and teaching. They ensure cost-effectiveness and customizability, and contribute to global collaboration in the education realm. Hence, education stakeholders face a critical challenge in locating suitable OERs that meet their needs and pedagogical objectives. To cater to this issue, researchers propose diverse digital solutions, one being recommender systems (RS). While a wide array of the suggested tools focused on learners only, this study introduces MOERID, an AI-powered OER Recommender Chatbot aimed at the teaching community. It aspires to facilitate resource discoverability, allowing instructors to save time and energy to concentrate on other pedagogical duties. MOERID engages NLP and recommendation filtering techniques to locate and deliver relevant OERs to instructors. The study describes the implementation of MOERID, highlights its efficacy and provides actionable recommendations for future research by outlining the research gaps.
Volume: 15
Issue: 3
Page: 3107-3117
Publish at: 2025-06-01

Content based image retrieval using visual geometric group19 with Jaccard similarity measure

10.11591/ijaas.v14.i2.pp430-438
Rajath Arakere Narayanaswamy , Vidyalakshmi Krishne Gowda
Content-based image retrieval (CBIR) is an important research area that focuses on emerging techniques for handling large image collections and enabling efficient retrieval. The main challenge of image retrieval is to extract relevant feature vectors for image description. Therefore, visual geometric group 19 (VGG19) with Jaccard is proposed in this research for CBIR. The VGG19 allows to capture of hierarchical features, and it is appropriate for texture and fine detail characteristics. It enables to production of robust and discriminative feature representations because of numerous convolutional layers. The Jaccard is utilized as a similarity measure among feature vectors by comparing the union and intersection of feature sets. It is helpful to manage sparse and higher-dimensional data that provides a fast and accurate image retrieval process. The Caltech 256 and Corel 1K datasets are considered and it is preprocessed by image resizing and normalization. The raw images are resized to ensure dataset similarity and normalized into the range of 0 and 1. The metrics such as recall, f-measure, and average precision are used to calculate the VGG19-Jaccard performance. The VGG19-Jaccard achieves average precision of 99.0 and 99.8% for Caltech 256 and Corel 1K datasets compared to the two-stage CBIR technique.
Volume: 14
Issue: 2
Page: 430-438
Publish at: 2025-06-01

Bridging technology and healthcare: user acceptance of a surgical site infection system

10.11591/ijaas.v14.i2.pp523-532
Afan Fatkhur Akhmad , Maria Ulfa
Surgical site infections (SSI) continue to be a problem for surgeons, and unfortunately, SSI information systems are underutilized. This study analyzed the user acceptance of the SSI information system based on the extended technology acceptance model (TAM2). A cross-sectional questionnaire-based study. The variables studied intention to use (IU), perceived ease of use (PEOU), demographic factors (FD), subjective norm (SN), Image (I), job relevance (JR), output quality (OQ), result demonstrability (RD), perceived usefulness (PU). Data were collected by filling out questionnaires and then analyzed using smart-partial least squares (PLS). In total, 61 nurses were included. Most respondents are aged 31-35 (26.23%), and most working periods are between 11-15 years (27.87%). There were significant positive effects on SN to PU (β=0.12; p 0.05). This study concluded that PEOU is the most influential variable in the IU the SSI information system.
Volume: 14
Issue: 2
Page: 523-532
Publish at: 2025-06-01

Road pavement deformation using remote sensing technique

10.11591/ijaas.v14.i2.pp345-351
Kishan Patel , Rajesh Gujar
The road surface reflects the status of the city’s infrastructure. Road safety and driving comfort can be affected by the rough surface. To minimize road hazards, pavement conditions must be periodically inspected for damaged surfaces. A quick and efficient data collection can be provided by the radar images. For a large spatial coverage, radar image provides a non-destructive data collection technique for analyzing road conditions and classifying distress. The surface distress can be correlated by analyzing the images collected from high-resolution cameras and satellites. This article outlines the applicability of synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) based images to manage and monitor pavement infrastructure. Therefore, the detection of deteriorating surfaces can be improved by analyzing the radar images timely. The results showed the deficiencies on the surface that can be used to mitigate bad pavement conditions and allow road users to use good road infrastructure with safety and comfort.
Volume: 14
Issue: 2
Page: 345-351
Publish at: 2025-06-01

Sign language recognition and classification using blended ensemble machine learning

10.11591/ijai.v14.i3.pp2035-2043
Akash Rajan Rai , Sujata Rajesh Kadu
An efficient sign language recognition system (SLR) is the most significant for hearing-impaired people for communication. The body movements and hand gestures are utilized to characterize the vocabulary in dynamic sign language. The SLR is a challenging problem because the computational model requires simultaneous spatial-temporal modelling for a number of sources. To overcome this problem, this research proposes the blended ensemble machine learning (ML) approaches for SLR. Initially, the Indian sign language (ISL) dataset is collected for evaluating the effectiveness of the model. Then, the pre-processing is done by using data augmentation and normalization techniques. Then, the pre-processed data is provided to the segmentation process which is done by using multi-threshold entropy function. Then, VGG-16 is used for the feature extraction process to extract the features and finally, classification is carried out using ensemble ML. An effectiveness of the proposed method is validated based on accuracy, precision, recall, and F1-score, wherein it achieves better results of 99.57%, 0.92%, 0.95%, and 0.99% as compared to the existing works like support vector machine (SVM) and convolutional neural network (CNN).
Volume: 14
Issue: 3
Page: 2035-2043
Publish at: 2025-06-01

Integrating gamification to increase users’ engagement to adhere COVID-19 interventions using extended TAM

10.11591/ijeecs.v38.i3.pp1936-1945
Mohd Hazim Afiq Kalana , Syahrul Nizam Junaini , Suriati Khartini Jali , Yusep Rosmansyah , Atina Putri , Ahmad Alif Kamal
Gamification has shown potential for enhancing motivation and engagement, yet its effectiveness in promoting adherence to COVID-19 preventive measures remains underexplored. With decreasing public attentiveness, this study examines the role of gamification in encouraging compliance with health protocols through an extended technology acceptance model (TAM) and structural equation modeling (SEM). A gamified mobile application was developed, incorporating features such as points, badges, and progress tracking, and was designed to appeal to younger audiences. Data collected from 150 secondary school students in Sarawak, Malaysia, indicated that perceived usefulness, perceived ease of use, and attitude toward the app significantly influenced engagement with COVID-19 preventive measures. Among these factors, perceived usefulness demonstrated the strongest effect on engagement (β = 0.424, t = 4.812, p < 0.001). The findings highlight the potential of gamification to enhance compliance with COVID-19 protocols.
Volume: 38
Issue: 3
Page: 1936-1945
Publish at: 2025-06-01

Modeling sentiment analysis of Indonesian biodiversity policy Tweets using IndoBERTweet

10.11591/ijai.v14.i3.pp2389-2401
Mohammad Teduh Uliniansyah , Asril Jarin , Agung Santosa , Gunarso Gunarso
This study develops and evaluates a sentiment analysis model using IndoBERTweet to analyze Twitter data on Indonesia’s biodiversity policy. Twitter data focusing on topics such as food security, health, and environmental management were collected, with a representative subset of 13,435 tweets annotated from a larger dataset of 500,000 to ensure reliable sentiment labels through majority voting. IndoBERTweet was compared to seven traditional machine-learning classifiers using TF-IDF and BERT embeddings for feature extraction. Model performance was assessed using mean accuracy, mean F1 score, and statistical significance (p-values). Additionally, sentiment analysis included word attribution techniques with BERT embeddings, enhancing relevance, interpretability, and consistent attribution to deliver accurate insights. IndoBERTweet models consistently outperformed traditional methods in both accuracy and F1 score. While BERT embeddings boosted performance for conventional models, IndoBERTweet delivered superior results, with p-values below 0.05 confirming statistical significance. This approach demonstrates that the model’s outputs are explainable and align with human understanding. Findings underscore IndoBERTweet’s substantial impact on advancing sentiment analysis technology, showcasing its potential to drive innovation and elevate practices in the field.
Volume: 14
Issue: 3
Page: 2389-2401
Publish at: 2025-06-01

A review of open-source energy system modeling tools

10.11591/ijaas.v14.i2.pp469-480
Nguyen Binh Khanh , Phuong Le Ngo , Luong Ngoc Giap , Truong Nguyen Tuong An , Trung Bui Tien , Tran The Vinh , Le Van Nghia , Tran Trong Dat
Nowadays, the transition to open markets, the rapid growth of renewable energy sources like wind and solar, and the shift towards electrification in transportation and industry for decarbonization have increased the demand for advanced energy system models with detailed spatial and temporal data. This paper utilizes a comprehensive literature review and selects a representative set of open-source tools for evaluation. A comparative analysis of 17 open-source energy system modeling tools and their commercial alternatives was conducted. The paper analyzes many open-source aspects such as code commits, updates, programming languages, license details, citations, and energy system modeling features such as power flows (PFs), continuation PF, dynamic analysis, short-circuit analysis, contingency analysis, transportation model, optimal PF (OPF), multi-period OPF, unit commitment (UC), investment optimization, and graphic user interface. Based on the results, the paper suggests appropriate tools used for according power/energy system analysis objective: MATPOWER for power system analysis and Python for power system analysis (PyPSA) for energy system analysis.
Volume: 14
Issue: 2
Page: 469-480
Publish at: 2025-06-01
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