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30,185 Article Results

An exploratory review on conceptualizing generative artificial intelligence literacy

10.11591/ijai.v15.i2.pp1023-1035
Mohammed Afandi Zainal , Mohd Effendi Mohd Matore , Siti Mistima Maat
Generative artificial intelligence (AI) has rapidly evolved, demanding new forms of literacy that go beyond traditional AI concepts. However, current definitions of generative AI literacy often overlook its unique challenges, including prompt engineering, critical evaluation of AI-generated outputs, and complex ethical considerations. This study addresses these gaps through an exploratory review of 20 peer-reviewed articles. These articles were identified using systematic searches across major academic databases and selected based on predefined inclusion criteria. The analysis reveals conceptual limitations in existing frameworks, particularly their lack of structure and their generalization of AI literacy. To overcome these issues, we propose a new competency framework adapted from Bloom’s taxonomy. The framework integrates three essential dimensions: technical proficiency, ethical responsibility, and societal awareness. It is organized into five progressive cognitive stages: understand, apply, analyze, evaluate, and create. This framework clarifies the distinct demands of generative AI literacy and can be implemented to guide curriculum design, professional training, and the development of generative AI literacy across sectors.
Volume: 15
Issue: 2
Page: 1023-1035
Publish at: 2026-04-01

MNetNCR: MobileNet model for efficient traditional Nusantara script character recognition

10.11591/ijai.v15.i2.pp1513-1528
Untari Novia Wisesty , Aditya Firman Ihsan , Mahmud Dwi Sulistiyo , Donni Richasdy , Prasti Eko Yunanto , Gamma Kosala , Arfive Gandhi , Febryanti Sthevanie
Preservation of traditional Nusantara scripts is very important because these traditional scripts are part of the cultural heritage that reflects the identity and history of the nation. This research proposed MobileNet for Nusantara character recognition (MNetNCR) model based on MobileNetV3 architecture to recognize traditional Nusantara scripts with lightweight, efficient architecture, and accurate recognition. The novel and comprehensive datasets for traditional Nusantara scripts have been curated in this research, that will later be stored digitally and can be used in further research. This novel dataset includes handwritten Balinese, Batak, Javanese, Lontara, and Sundanese scripts, each with unique visual characteristics. The proposed MNetNCR model is highly effective in recognizing characters, achieving F1-scores of 0.9934 for Balinese, 0.9450 for Batak, 0.9788 for Javanese, 0.9936 for Lontara, and 0.9961 for Sundanese scripts, according to the experimental results. The MNetNCR model built in this research has been proven to be effective and efficient in recognizing traditional scripts accurately. It also supports the preservation and promotion of the nation's cultural and historical heritage.
Volume: 15
Issue: 2
Page: 1513-1528
Publish at: 2026-04-01

The effects of data imbalance on fraud detection model accuracy

10.11591/ijai.v15.i2.pp1402-1408
Rusma Anieza Ruslan , Nureize Arbaiy , Pei-Chun Lin
Machine learning (ML) model performance is often assessed by accuracy, but the quality and balance of data also play crucial roles. Imbalanced datasets, where the minority class has fewer samples than the majority class, can lead to biased predictions favoring the majority class. This study addresses the issue of class imbalance through resampling techniques, including random undersampling (RUS) and random oversampling (ROS), specifically applied to a fraud detection dataset. We classify the resampled datasets using random forest (RF) and gradient boosting (GB) models. Our findings indicate that the RF model, when combined with ROS, achieves an accuracy of 97.4%, surpassing the 96.1% accuracy of the GB model with RUS. This approach demonstrates the importance of addressing class imbalance to improve prediction accuracy in ML.
Volume: 15
Issue: 2
Page: 1402-1408
Publish at: 2026-04-01

Comparative deep learning study for downy mildew detection in vegetables

10.11591/ijai.v15.i2.pp1719-1732
Supreetha Shivaraj , Manjula Sunkadakatte Haladappa
Several vegetable crops are affected by downy mildew, a major foliar disease resulting in notable reductions in yield. For sustainable agriculture and disease prevention, early and precise detection is crucial. To be able to detect downy mildew in five varied vegetables—bitter gourd, bottle gourd, cauliflower, cucumber (Rashid), and cucumber (Sultana)—this study evaluates three deep learning architectures: VGG19, DenseNet201, and MobileNetV2. This work focuses on imbalanced datasets collected from several sources, in opposition to prior work that depended on balanced laboratory datasets. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models shortly after they were trained using transfer learning, data augmentation, and 5-fold cross-validation. Model focus regions were assessed by using gradient-weighted class activation mapping (Grad-CAM) visualizations, and statistical reliability was assessed based on paired t-tests and Wilcoxon signed-rank tests. By achieving mean accuracies above 98% and statistically significant results (p <0.05) on cucumber datasets, DenseNet201 accomplished superior performance. Despite attaining slightly lower accuracy (89.6–100%), MobileNetV2 offered the smallest model size (12.9 MB) and minimum inference time (85 ms). The proposed approach demonstrated a transparent, generalizable, and computationally efficient deep learning pipeline for precision agriculture’s real-time downy mildew detection.
Volume: 15
Issue: 2
Page: 1719-1732
Publish at: 2026-04-01

Accurate stroke area classification using extreme gradient boosting with multi-feature extraction

10.11591/ijai.v15.i2.pp1390-1401
Kavikondala Praveen Kumar Rao , Maha Lakshmi Bondla , Bommaraju Srinivasa Rao , Ambidi Naveena , K. V. Balaramakrishna , Srinivasarao Goda
Stroke, one of the most common neurological disorders leading to long-term disability and mortality, requires accurate detection of affected brain regions for timely treatment planning. However, conventional deep learning models face challenges in achieving precise segmentation and robust classification due to noisy inputs, weak feature representation, and poor generalization. To address these gaps, this study introduces a hybrid framework that integrates the ConvNeXt architecture for stroke region segmentation with XGBoost based classification, strengthened through three complementary feature extraction methods: local binary patterns (LBP), adaptive threshold directional binary gradient matrix (AT-DBGM), and wavelet packet transform (WPT). These methods capture textural, directional, and multi resolution features, which are concatenated into a stacked vector and classified using XGBoost. Preprocessing steps, including normalization and resizing, ensure improved input consistency. Experimental evaluations on benchmark stroke imaging datasets show that the proposed framework achieves 98.56% Dice similarity coefficient (DSC), 12.96 mm Hausdorff distance (HD), 99.12% accuracy, 98.69% sensitivity, 99.06% specificity, 98.98% precision, and 98.85% F1-score.
Volume: 15
Issue: 2
Page: 1390-1401
Publish at: 2026-04-01

An intelligent and explainable IoT-Edge-Cloud architecture for real-time water quality monitoring

10.11591/ijai.v15.i2.pp1109-1120
Sara Bouziane , Badraddine Aghoutane , Aniss Moumen , Anas El Ouali , Ali Essahlaoui , Abdellah El Hmaidi
Continuous and reliable monitoring of water quality is critical for early detection of environmental deterioration, yet conventional monitoring approaches are often slow and lack timely data availability. This study proposes an intelligent and explainable internet of things (IoT)–Edge–Cloud architecture to monitor water quality in real time, using IoT sensing, edge based artificial intelligence (Edge AI), cloud-stream processing, and explainable artificial intelligence (XAI). The system calculates the water quality index (WQI) directly at the edge and predicts its evolution using a stacking ensemble model trained on physicochemical measurements taken from the Moulouya River Basin in Morocco. An explainability module based on Shapley additive explanations (SHAP) values gives a clearer image of the contribution of various parameters to WQI predictions, providing transparency of the features, which builds trust in the model’s output. The proposed architecture was implemented as an end-to-end prototype and validated using a simulation-based experimental that mimicked realistic sensor dynamics and connectivity interruptions. The experimental results show strong predictive performance (R² =0.945), stable system operations, and reliable interpretability highlighting the potential of the proposed approach for scalable, intelligent, and transparent environmental monitoring.
Volume: 15
Issue: 2
Page: 1109-1120
Publish at: 2026-04-01

Sentiment-aware user-item recommendation combining weighted XGBoost and optimized similarity metrics

10.11591/ijai.v15.i2.pp1851-1862
Snehal Bhogan , Vijay S. Rajpurohit , Sanjeev S. Sannakki
User-item recommendation systems play a vital role in enhancing personalized digital experiences across e-commerce and social media platforms. Traditional recommendation approaches, such as collaborative filtering (CF) and content-based filtering (CBF), often suffer from challenges like data sparsity, cold-start issues, and limited contextual understanding. Sentiment-aware recommendation systems have emerged as a promising solution by incorporating emotional insights extracted from user reviews, thereby improving recommendation accuracy and personalization. This study proposes a novel sentiment-aware user-item recommendation system (SAUIRS) framework that integrates optimized term frequency inverse document frequency (O-TF-IDF), parameterized bidirectional encoder representations from transformers (P-BERT), weighted extreme gradient boosting (WXGBoost), and an optimized similarity metrics model. The optimized TF-IDF enhances feature selection, reducing dimensionality while preserving relevant textual information. P-BERT, a fine-tuned BERT model, improves sentiment classification accuracy by leveraging deep contextual embeddings. WXGBoost further refined sentiment predictions, addressing class imbalance and enhancing model robustness. The extracted sentiment information is incorporated into an optimized similarity metrics model to improve recommendation precision by aligning user preferences with sentiment-driven insights. Extensive experiments conducted on Amazon benchmark datasets demonstrate the superior performance in terms of accuracy, root mean square error (RMSE), and mean absolute error (MAE) of the proposed framework compared to state-of-the-art recommendation models.
Volume: 15
Issue: 2
Page: 1851-1862
Publish at: 2026-04-01

Multimodal facial expression recognition using residual mogrifier long short-term memory

10.11591/ijai.v15.i2.pp1566-1577
Mamatha Kariyappa Rajanna , Thejaswini Shankar , Rashmi Narasimhamurthy , Nandhini Annivedu Lakshmanan , Hariprasad S. Ananthapadmanabharao
Multimodal facial expression recognition aims to improve emotion analysis by integrating visual, audio, and textual cues to achieve accuracy and robustness. However, effectively recognizing facial expressions across video, text, and audio presents challenges due to inconsistencies in how emotions are expressed among these modalities. To overcome this issue, this research proposes a residual mogrifier long short-term memory (RMLSTM) model to enhance robustness in multimodal facial expression recognition. By integrating residual connections into the long short-term memory (LSTM), the model improves its ability to capture complex dependencies among various modalities, including video, text, and audio. The residual connection overcomes the vanishing gradient problem and ensures stable training with better gradient flow in deeper networks. The mogrifier mechanism refines the input features dynamically, enhancing feature interaction and alignment across modalities. The RMLSTM achieves 99.57% and 97.83% accuracy on the SAVEE and YouTube datasets, respectively, outperforming both the mel-frequency cepstral coefficients time-domain feature with iterative dilated convolutional neural network (MFCCT-1DCNN) and attention-based multi-modal popularity prediction model of short-form videos (AMPS).
Volume: 15
Issue: 2
Page: 1566-1577
Publish at: 2026-04-01

IndoBERT for educational assessment: comparative analysis of transformer models in Indonesian question generation

10.11591/ijai.v15.i2.pp1804-1813
Handaru Jati , Yuniar Indrihapsari , Pradana Setialana , Danang Wijaya , Satya Adhiyaksa Ardy , Dhista Dwi Nur Ardiansyah
This study asks whether a monolingual encoder can realistically outperform multilingual and larger transformer models for Indonesian automatic question generation (AQG) when all models share the same training budget. We compare Indonesian bidirectional encoder representations from transformers (IndoBERT), multilingual BERT (mBERT), and BERT-large using a single fine-tuning pipeline with answer highlighting, applied to an Indonesian version of TyDiQA-GoldP and a 20,000 translated subset of SQuAD 2.0. We evaluate model quality using bilingual evaluation understudy score n-gram 4 (BLEU-4), metric for evaluation of translation with explicit ordering (METEOR), and ROUGE-Lincoln (ROUGE-L). IndoBERT consistently achieves the best scores on both datasets (e.g., BLEU-4 of 19.69 on TyDiQA-GoldP and 3.79 on the SQuAD 2.0 subset) while requiring less computation than mBERT and BERT-large. Our results show that language-specific pretraining gives clear advantages for Indonesian AQG, yielding higher accuracy at lower computational cost than multilingual or larger encoders. The work closes a gap in Indonesian AQG benchmarking by providing the first head-to-head comparison of IndoBERT, mBERT, and BERT-large under a shared fine-tuning and evaluation protocol. For educational assessment, the findings offer a practical recipe for building deployable AQG systems on mid-range GPUs that generate higher quality questions without prohibitive training or inference budgets.
Volume: 15
Issue: 2
Page: 1804-1813
Publish at: 2026-04-01

A hybrid model for enhanced aspect-based sentiment analysis using large language models

10.11591/ijai.v15.i2.pp1825-1838
Mohammed Ziaulla , Arun Biradar
Aspect-based sentiment analysis (ABSA) is a crucial task within natural language processing (NLP), enabling fine-grained opinion mining by identifying sentiments associated with specific aspects of a product or service. While transformer-based models like bidirectional encoder representations from transformers (BERT) have improved sentiment classification, they still struggle with limited contextual adaptability, especially in customer reviews containing complex expressions. Most existing approaches rely heavily on benchmark datasets such as semantic evaluation (SemEval) and multi-aspect multi-sentiment (MAMS), which do not fully capture the diversity of real-world review scenarios. Hence, this research addresses these limitations by proposing a novel hybrid model, called as hybrid-BERT (H-BERT), that integrates span-aware BERT (SpanBERT) with bidirectional long short-term memory (BiLSTM), conditional random field (CRF), and large language models (LLMs). The objective is to enhance aspect extraction and sentiment classification performance using both annotated and synthetic data. The methodology includes preprocessing, hybrid model training, and evaluation using the SemEval 2014 dataset. Experimental results show that H-BERT achieved 90.58% accuracy and 90.56% F-score in the laptop domain and 91.21% accuracy with a 92.03% F-score in the restaurant domain. These results outperform existing models, confirming H-BERT’s robustness and effectiveness. In conclusion, H-BERT improves sentiment understanding in customer reviews.
Volume: 15
Issue: 2
Page: 1825-1838
Publish at: 2026-04-01

Blockchain-enabled framework using diversity mutation with siberian tiger optimization for offloading in fog computing

10.11591/ijai.v15.i2.pp1371-1380
Srikanta Murthy Rajini , Reginald Shilpa
Fog computing has developed as a promising framework to support latency sensitive internet of things (IoT) applications for mobile devices operating in dynamic environments. During the offloading process, malicious activities interrupt the existing methods, which increases the execution time. Therefore, this research proposes a diversity mutation with siberian tiger optimization (DM-STO) for computation offloading in blockchain based fog computing. The blockchain is used to secure offload and attain quality of service (QoS) mobile users with less energy consumption and execution time. The DM-STO can balance workloads among local devices and fog servers. The diversity mutation operation improves the exploration ability to dynamic network conditions, leading to efficient computational offloading in fog computing. The execution time, service cost and energy consumption are evaluated to calculate the performance of the proposed DM-STO with varying numbers of IoT requests such as 50, 100, 200, and 300. For 50 IoT requests with a fixed fog server of 10, the DM-STO achieves an execution time of 18 s, a service cost of 10$ and energy consumption of 5 mJ compared to the BAT algorithm.
Volume: 15
Issue: 2
Page: 1371-1380
Publish at: 2026-04-01

Adaptive control of ball and beam system using SNA-PID combined with recurrent fuzzy neural network identifier

10.11591/ijai.v15.i2.pp1202-1210
Minh-Thanh Le , Chi-Ngon Nguyen
The ball and beam system is a nonlinear and inherently unstable single input, multiple-output (SIMO) system, which poses significant challenges for control design. Intelligent control algorithms are often applied to autonomously control complex systems when there are changes in parameters or the control environment. Therefore, in this paper, we research and develop two methods: proportional integral derivative (PID) and single neuron adaptive (SNA)-PID-recurrent fuzzy neural network identifier (RFNNI) to control the ball and beam system. Simulation results on MATLAB/Simulink show that the SNA-PID-RFNNI controller provides a more stable output signal than the traditional PID controller, with minimal overshoot and a settling time of about 15 seconds. Next, we will conduct real-time experiments on the object using the proposed algorithm through the MEGA2560 control board with an ultrasonic positioning mechanism.
Volume: 15
Issue: 2
Page: 1202-1210
Publish at: 2026-04-01

Fetal organ detection using feature enhancement with attention and residual block

10.11591/ijai.v15.i2.pp1593-1604
Nuswil Bernolian , Siti Nurmaini , Ade Iriani Sapitri , Annisa Darmawahyuni , Muhammad Naufal Rachmatullah , Bambang Tutuko , Firdaus Firdaus
The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Volume: 15
Issue: 2
Page: 1593-1604
Publish at: 2026-04-01

Automated bacteria and fungi classification using convolutional neural network on embedded system

10.11591/ijai.v15.i2.pp1132-1142
Tarik Bouganssa , Maryem Ait Moulay , Samar Aarabi , Abedelali Lasfar , Abdelatif EL Afia
In this study, we created and applied novel concepts for hardware-based image identification and categorization. For artificial intelligence (AI) and image recognition applications, this includes putting algorithms for recognizing colors, textures, and shapes into practice. Our contribution uses an embedded device with a camera and a microcomputer (Raspberry-Pi4 type) to replace the optical assessment of Petri dishes. Our object recognition system processes images efficiently by using a state-of-the-art kernel function and a new neighborhood architecture. Using the well-known convolutional neural network (CNN) architecture, YOLOv8, as a pre-trained model, we evaluated the proposed CNN-based method for object recognition in a number of demanding scenarios. Several Petri plates, uncontrolled settings, and different backgrounds and illumination were used to evaluate the technology. Our dynamic mode integrates a CNN network with an attention mask to highlight the traits of bacteria and fungi, ensuring robust recognition. We implemented our algorithm on a Raspberry Pi 400, connected to a CMOS 3.0 camera sensor and a human-machine interface (HMI) for instant display of results.
Volume: 15
Issue: 2
Page: 1132-1142
Publish at: 2026-04-01

Identification of chemical markers for species differentiation in Aquilaria essential oils using self-organizing maps

10.11591/ijai.v15.i2.pp1339-1348
Nur Athirah Syafiqah Noramli , Muhammad Ikhsan Roslan , Noor Aida Syakira Ahmad Sabri , Nurlaila Ismail , Zakiah Mohd Yusoff , Mohd Nasir Taib
This study analyzes the chemical diversity of essential oils from four Aquilaria species, A. beccariana, A. malaccensis, A. crassna, and A. subintegra, which are important sources of agarwood used in perfumery and traditional medicine. Despite their economic and ecological value, the chemical profiles of these species remain insufficiently characterized, hindering accurate species differentiation and resource management. This research aims to identify distinctive chemical patterns to improve species classification. Self-organizing maps (SOMs) were employed to analyze complex chemical composition data and to identify significant compounds responsible for species separation. The analysis revealed several compounds with strong discriminatory power and species-specific distribution patterns, with compounds C, D, and E identified as the most significant markers. These findings demonstrate substantial biochemical diversity among Aquilaria species and confirm the effectiveness of SOM for essential oil profiling. The results support improved species identification and have important implications for ecological conservation, sustainable agarwood management, and pharmacological development.
Volume: 15
Issue: 2
Page: 1339-1348
Publish at: 2026-04-01
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