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31,456 Article Results

Research themes and trends in the field of blockchain engineering: a topic modelling analysis

10.11591/ijai.v15.i2.pp1863-1875
Dinara Zhaisanova , Madina Mansurova
This study employed topic modeling to identify key research themes in blockchain engineering and examined how these themes have evolved over time. The dataset of collected abstracts from 3,665 relevant papers of Web of Science (WoS) core collection for the period from 2019 to 2024 was analyzed with latent Dirichlet allocation (LDA) approach. Based on the results of the topic development trends analysis, the topics collectively highlight the evolving landscape of technologies such as blockchain, smart contracts, the internet of things (IoT), and edge computing, focusing on their integration and impact across sectors like finance, healthcare, supply chain management, and energy systems. It offers valuable insights and implications for research related to blockchain engineering. Latent semantic indexing (LSI) provided further understanding by highlighting strong connections between specific topics, such as energy trading, supply chains, and medical applications. A comparison of LDA and LSI topics revealed overlapping themes, which supports the reliability of the topic structure identified by LDA.
Volume: 15
Issue: 2
Page: 1863-1875
Publish at: 2026-04-01

Unified voting-based ensemble learning for rice leaf disease detection using improved pretrained models

10.11591/ijai.v15.i2.pp1646-1663
Govindarajan Subburaman , Mary Vennila Selvadurai
As a staple food for a large portion of the global population, rice is particularly susceptible to leaf diseases that adversely affect its yield and overall quality. This study utilizes four pretrained convolutional neural network (CNN) models to construct a unified voting-based ensemble approach for rice leaf disease classification. The models include VGG16, DenseNet121, InceptionV3, and Xception. The dataset used in this study was collected from Kaggle and further enriched with images obtained from Google sources. It comprises a total of 4,000 images categorized into six classes: bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves. It was split into training (327 images/class), validation (140 images/class), and testing (200 images/class). Images were normalized to [0,1] and augmented through rotation, flipping, shifting, shear, zoom, brightness, and channel adjustments to improve generalization. Individually, the fine-tuned models achieved accuracies of 91.3% (VGG16), 95.6% (DenseNet121), 92.1% (InceptionV3), and 89.8% (Xception). The ensemble leveraged majority voting (93.6%), weighted voting (96.5%), and soft voting (97%), yielding an absolute gain of 1.4% over the best individual model and 4.8% over the average of all models. To our knowledge, this is the first ensemble combining these four architectures with unified voting for identifying diseases in rice leaves, delivering a scalable and computationally efficient solution suitable in advance diagnosis and timely execution in agricultural settings with limited resources.
Volume: 15
Issue: 2
Page: 1646-1663
Publish at: 2026-04-01

TMA-Net: a transformer-based multi-modal attention network for abnormal behavior detection

10.11591/ijai.v15.i2.pp1441-1450
Huong-Giang Doan , Ngoc-Trung Nguyen
Abnormal behavior detection in crowded environments remains challenging due to complex motion patterns, occlusions, and domain variability. This paper presents transformer-based multi-modal attention network (TMA-Net), a unified framework that integrates red, green, and blue (RGB), optical flow (OF), and heat map (HM) modalities through a dual-stage attention fusion mechanism. The system employs you only look once version 11 (YOLOv11) for human localization and vision transformer (ViT)-B/16 for feature encoding, followed by intra-modal self-attention and cross-modal fusion to capture fine-grained spatial–temporal and motion energy dependencies. Extensive experiments on six public benchmarks as UMN, Crowd-11, UBNormal, ShanghaiTech, CUHK Avenue, UCSD Ped2, and EPUAbN dataset, demonstrate that TMA-Net achieves up to 97.5% area under the curve (AUC) and 96–100% accuracy, outperforming previous other state-of-the-art approaches. These results highlight the framework’s strong generalization and robustness across both single- and cross-dataset evaluations, underscoring its potential for reliable deployment in real intelligent surveillance systems.
Volume: 15
Issue: 2
Page: 1441-1450
Publish at: 2026-04-01

Automated classification of apple bruises from hyperspectral images: an approach for fruit quality assessment

10.11591/ijai.v15.i2.pp1381-1389
Peddireddy Venkateswara Reddy , Alaguchamy Parivazhagan
Apple bruise detection plays a crucial role in post-harvest quality control; however, conventional manual inspection remains labor-intensive, subjective, and unsuitable for large-scale industrial deployment. This study proposes an automated classification framework for identifying bruised regions in apples using hyperspectral imaging combined with deep learning and adaptive optimization techniques. The proposed model integrates a long short-term memory (LSTM) network optimized using an adaptive sand cat swarm optimization (ASCSO) algorithm, along with a ResNet-50 feature extraction backbone. The adaptive behavior embedded within ASCSO dynamically adjusts the optimization parameters to enhance convergence and prevent premature stagnation during LSTM hyperparameter tuning. Hyperspectral images were processed to extract relevant spectral–spatial features, which were subsequently fed into the optimized classifier. Experimental evaluations demonstrate that the proposed hybrid model significantly outperforms conventional and baseline deep learning approaches, achieving a classification accuracy of 98.0% while maintaining robustness across varying bruise patterns and intensity levels. The results highlight the effectiveness of combining hyperspectral imaging with adaptive deep learning optimization for high-precision fruit quality assessment. This research contributes a reliable, scalable solution for automated bruise detection and quality grading in the fruit supply chain, offering strong potential to reduce post-harvest losses and improve operational efficiency in the agro-food industry.
Volume: 15
Issue: 2
Page: 1381-1389
Publish at: 2026-04-01

Hybrid machine learning for imbalanced lettuce disease classification

10.11591/ijai.v15.i2.pp1783-1789
Fazlur Ihzanurahman , Wayan Firdaus Mahmudy
This study investigates a hybrid machine learning framework combining EfficientNet-B3 feature extraction with classical classifiers for lettuce disease classification under conditions of extreme class imbalance. The system utilizes EfficientNet-B3 to extract high-dimensional feature embeddings from 2,337 images, which are subsequently classified using support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN). Although the proposed SVM-based model achieves a high overall accuracy of 94.01%, experimental results reveal a substantial performance discrepancy compared to the macro F1-score of 37.94%. This critical gap indicates that while the model successfully identifies the majority classes, it fails to recognize rare disease categories with limited samples. Theoretical analysis suggests that while SVM handles high-dimensional feature spaces more effectively than RF and KNN, the deep features extracted are biased toward majority class characteristics. These findings highlight the severe limitations of accuracy-centric evaluation in agricultural diagnostics and demonstrate that deep feature extraction alone is insufficient to guarantee robust detection for minority pathologies. The study concludes that relying on aggregate accuracy can mask diagnostic failures, emphasizing the urgent need for per-class performance analysis and data-level mitigation strategies in future research.
Volume: 15
Issue: 2
Page: 1783-1789
Publish at: 2026-04-01

Breast cancer detection using residual DenseNets in deep learning

10.11591/ijai.v15.i2.pp1632-1645
Naganandini Gururajarao , Vishwanath R. Hulipalled
Breast cancer, the leading cause of cancer-related deaths among women globally, requires a prompt and precise diagnosis in order to increase survival rates via therapy. There is a possibility of bias and inconsistency in the results of traditional diagnostic procedures like mammography, ultrasound, and histological testing since they rely on the expertise of radiologists and pathologists. There are exciting new opportunities for breast cancer diagnostics to be enhanced by artificial intelligence (AI) and deep learning. The purpose of this research is to examine the feasibility of using convolutional neural networks (CNNs) and residual dense networks (ResDenseNets) used for breast cancer automated detection in medical images. Because of their superior capacity to learn hierarchical features from raw image data, CNNs are ideal for medical image interpretation. By including residual connections, which allow for the training of considerably deeper models, ResDenseNets—an extension of CNNs—mitigate the problem of vanishing gradient in deep networks. ResDenseNet and CNNs considerably enhance the accuracy of breast cancer diagnosis in comparison to conventional approaches, according to the findings. Notably, ResDenseNets outperform other types of networks because they are able to learn intricate and nuanced properties directly from the data.
Volume: 15
Issue: 2
Page: 1632-1645
Publish at: 2026-04-01

2D-CNN-GACL-ECGNet graph attention: a robust framework for electrocardiogram-based stress detection

10.11591/ijai.v15.i2.pp1529-1538
P. Kavitha , L. Shakkeera
Early detection of cardiovascular diseases (CVDs) via electrocardiogram (ECG) classification during physiological stress is critical and remains challenging due to stress-induced morphological variability, noise from ambulatory settings, and inter-class ambiguities. Existing models, such as 1D signal-based models with convolutional neural networks (CNNs) and graph convolutional networks (GCNs), struggle to adapt to dynamic stress conditions and generate interpretable insights. In response, we propose 2D CNN and graph attention network (GAT) for optimizer. The model 2D-CNN and GACL-ECG-Net, an innovative framework integrating GATs with adaptive contrastive learning (ACL) and morpho-temporal graph construction. Key innovations include 2D-CNN denoising, 2D transformation, dynamic morpho-temporal graphs modeling ECG beats as nodes with hybrid edges (70% morphological similarity, 30% temporal proximity), and stress-adaptive contrastive loss with learnable margins on stress-conditioned labels, reducing class ambiguity by 18%. Multi-head attention mechanisms provide interpretable heatmaps aligned with cardiologist annotations (κ =0.82) and are evaluated using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, wearable stress and affect detection (WESAD) dataset for emotional stress, and stress at work, knowledge work (SWELL-KW) dataset for cognitive stress. 2D-CNN-GACL-ECG-Net achieves state-of-the-art performance with 98.7% F1-score (MIT-BIH), 94.2% (WESAD), and 92.8% (SWELL-KW), outperforming CNN-bidirectional long short-term memory (BiLSTM) and GCN baselines by 95%. The framework is computationally efficient and clinically validated for wearable health monitoring.
Volume: 15
Issue: 2
Page: 1529-1538
Publish at: 2026-04-01

Usability analysis of the individual creativity assessment tool using the adjusted system usability scale

10.11591/ijai.v15.i2.pp1955-1962
Mohamad Rahimi Mohamad Rosman , Noor Arina Md Arifin , Siti Aishah Mokhtar , Nur Ainatul Mardiah Mat Nawi , Huda Hamidon , Salliza Md Radzi
Creativity is a critical element in the learning environment, which leads to innovation and research advancement in higher education. However, assessing creativity is challenging due to its diverse nature and the lack of standardized tools. The existing assessment tools often overlook the critical role of organizational culture in shaping individual creativity within academic settings. To address this gap, the individual creativity assessment tool (i-CAT) was developed based on the framework of organizational culture to assess its contribution to creativity among Malaysian academicians. This study aimed to i) assess the usability of i-CAT and ii) determine the significant effect of demographic factors on its usability assessment. A quantitative methodology, utilizing expert sampling and the system usability scale (SUS), was employed as the primary evaluation method. 20 experts with relevant professional and academic experience were selected for the validation. The results showed excellent usability, with 95% of experts rating the information system as functionally acceptable. A one way analysis of variance (ANOVA) found no significant difference in usability based on profession or education levels, but a significant difference was observed for experience levels. These findings confirm that i-CAT is a functional, user-friendly, and culturally relevant tool for creativity assessment within Malaysia’s higher education institutions.
Volume: 15
Issue: 2
Page: 1955-1962
Publish at: 2026-04-01

AI-induced fatigue among students in higher education: a latent profile analysis

10.11591/ijai.v15.i2.pp1963-1971
Dynah D. Soriano , Jordan L. Salenga , John Paul P. Miranda , Juvy C. Grume , Emerson Q. Fernando , Jr., Amado B. Martinez , Raymond A. Cabrera , Jaymark A. Yambao
The integration of artificial intelligence (AI) tools in education offers significant benefits but also introduces challenges, including AI-induced fatigue among students. This study aimed to classify students’ experiences with AI tools using latent profile analysis (LPA). A quantitative cross sectional design and referral approach were used to collect survey data from 388 college students who actively used AI tools for academic purposes from November to December 2024. The survey measured AI usage intensity, AI literacy, self-efficacy, perceived usefulness, cognitive load, technostress, sleep quality, general fatigue levels, and attitude toward AI. Descriptive results indicated moderate levels of AI usage intensity, AI literacy, perceived usefulness, cognitive load, sleep quality, and general fatigue, with technostress and attitude toward AI also at moderate levels. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and profile size adequacy, and expert review supported the retained six-profile structure. The LPA identified six interpretable user groups: competent but sleep-deprived users, overwhelmed and high-strain users, stable moderate users, strained moderate users, high intensity strained users, and low-strain selective users. The findings show differences in patterns of competence, strain, fatigue, and sleep outcomes associated with AI tool use, which supports the development of profile specific strategies to manage technostress, cognitive load, fatigue, and sleep disruption among higher education students.
Volume: 15
Issue: 2
Page: 1963-1971
Publish at: 2026-04-01

Deep learning for mental health analysis: long short-term memory approach to text-based condition classification

10.11591/ijai.v15.i2.pp1762-1770
Zaqqi Yamani , Dinda Lestarini , Sarifah Putri Raflesia , Purwita Sari , Ghita Athalina
The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.
Volume: 15
Issue: 2
Page: 1762-1770
Publish at: 2026-04-01

Exponential long short-term memory with Levy flight optimization for lung nodule classification

10.11591/ijai.v15.i2.pp1451-1463
Kaliba Gowthami , Kamalakannan Jayaseelan
Lung cancer, which commonly appears as lung nodules is a deadly type of cancer that develops in a lung. Early detection of lung cancer is critical and challenging task due to presence of overlapping structures, which make it challenging to differentiate the benign and malignant regions. This research proposes long short-term memory (LSTM) with exponential linear unit (ELU) method for the classification of different classes of lung nodules. The hyperparameters of the LSTM network are optimized using the developed dynamic Levy flight – Archimedes optimization algorithm (DLF-AOA), which effectively identifies the optimal parameters for classification. The ResNet-18 method is used for the extraction of high-level features to differentiate various classes of lung nodules. Furthermore, Bayesian active contour (BAC) is employed for the segmentation of images as containing cancerous and non-cancerous regions of lung nodules. The LSTM with ELU method achieves 98.56% accuracy, 97.54% sensitivity, 98.22% specificity, 96.93% precision, 96.33% F1-score, and 1.44 error rate in IQ-OTH/NCCD lung cancer dataset.
Volume: 15
Issue: 2
Page: 1451-1463
Publish at: 2026-04-01

Recognition system based on artificial vision using OpenCV for discarding and detecting ceramics with defects

10.11591/ijai.v15.i2.pp1166-1173
Fernando Alvarado , Ricardo Yauri
Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.
Volume: 15
Issue: 2
Page: 1166-1173
Publish at: 2026-04-01

Double-hop of reconfigurable intelligent surfaces-aided for wireless optical link under log-normal fading channels

10.11591/ijai.v15.i2.pp1174-1180
Duong Huu Ai , Van Loi Nguyen , Khanh Ty Luong
In optical wireless communication (OWC), the reconfigurable intelligent surfaces (RIS) are used to manipulate optical signals by controlling the phase shifts or amplitude of reflected beams, which helps improve signal quality. RIS units can be tailored to increase the strength and reliability of the communication link, especially in challenging fading conditions. The double-hop scenario involves two RIS-assisted segments, such as transmitter to RIS-1 and RIS-1 to RIS-2 or a receiver. Each hop encounters log-normal fading, which impacts the overall link performance. Log-normal fading models the irradiance fluctuation caused by turbulence, which is significant in free-space optical (FSO) systems, this fading model assumes that the received optical signal’s amplitude varies with a log-normal distribution, making it more suited for weak to moderate turbulence. Numerical results are obtained under different of link distance, subcarrier quadrature amplitude modulation (QAM) is displayed quantitatively illustrate the average symbol error rate in the absence of RIS and with double-hop of RIS.
Volume: 15
Issue: 2
Page: 1174-1180
Publish at: 2026-04-01

Transformer-based Hindi image description and storytelling using enhanced attention and FastText embeddings

10.11591/ijai.v15.i2.pp1771-1782
Anjali Sharma , Mayank Aggrwal , Jitin Khanna
This work presents a novel image description generation framework that combines a Transformer-based encoder-decoder architecture with a custom squeeze-and-excitation (SE) attention block integrated into an EfficientNet feature extractor. The decoder uses FastText embeddings specifically trained for Hindi and is evaluated on the Microsoft common objects in context (MS-COCO) dataset. To improve the captioning process, the model incorporates a generative pre-trained transformer (GPT) module to generate narrative descriptions based on the initial captions and applies multiple similarity metrics to assess output quality. The proposed system significantly outperforms existing methods, achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1 to BLEU-4: 83.24, 73.17, 64.56, and 58.22), a consensus-based image description evaluation (CIDEr) score of 81.41, an F1 score of 90.29, and a metric for evaluation of translation with explicit ordering (METEOR) score of 81.18, indicating strong caption accuracy. Furthermore, the model achieves low error rates, with a word error rate (WER) of 15% and a character error rate (CER) of 11%. This work highlights the challenges of applying large-scale datasets like MS-COCO to resource-limited languages and demonstrates the effectiveness of integrating FastText embeddings with transformer-based models for Hindi image captioning.
Volume: 15
Issue: 2
Page: 1771-1782
Publish at: 2026-04-01

Hybrid kernel support vector machine with cuckoo search optimization for malaria detection from blood smear images

10.11591/ijai.v15.i2.pp1316-1326
Sri Huning Anwariningsih , Indrarini Dyah Irawati
Microscopic image-based malaria detection still struggles to capture complex features due to variations in lighting and color. The support vector machine (SVM) method is often used in medical image detection, but its performance depends heavily on the selection of optimal kernel and hyperparameters (C and gamma). Conventional approaches, with single kernels and manual tuning, have limitations in capturing both spatial information and color distribution simultaneously. Therefore, this research proposes hybrid kernel support vector machine-cuckoo search algorithm (HKSVM-CSA) method that combines the radial basis function (RBF) kernel and histogram intersection for SVM, along with hyperparameter optimization using the CSA. The dataset used is malaria cell images, which contains parasitized and uninfected images of blood cells. The proposed method comprises five main steps: dataset preparation, feature extraction, HKSVM, hyperparameter optimization, and model evaluation. Experiments demonstrate that the proposed model achieves 94% accuracy, 93% sensitivity, 94% specificity, and area under the curve (AUC) of 0.98, which is significantly better than standard SVM, SVM-genetic algorithm (GA), and k-nearest neighbors (KNN). These results show that combining kernel and CSA significantly improves detection accuracy. This approach is promising for image-based automatic systems for infectious disease diagnosis.
Volume: 15
Issue: 2
Page: 1316-1326
Publish at: 2026-04-01
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