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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

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

IoT-enabled smart nutrition scale using fuzzy logic for dietary assessment and recommendation

10.11591/ijai.v15.i2.pp1194-1201
Wahyu Wijaya Widiyanto , Edy Susanto , Sri Suparti
Childhood malnutrition, particularly stunting, remains a major public health challenge that requires preventive and technology-supported nutritional interventions. This study presents an IoT-enabled smart nutrition scale integrated with fuzzy logic to support real-time dietary assessment and personalized recommendation. The system combines IoT-based sensing, mobile and web applications, and a fuzzy inference engine that evaluates child profiles and food composition data to generate nutritional adequacy scores and tailored dietary guidance. Experimental validation demonstrates high measurement accuracy of the sensing system, achieving a strong linear correlation (R² ≈0.9995). Comparison with expert nutritionist assessments shows strong agreement, supported by low error values (mean absolute error (MAE) =2.96; root mean square error (RMSE) =3.41), and Bland–Altman analysis. Usability evaluation involving community health workers and caregivers yields an excellent system usability scale (SUS) score, indicating strong acceptance for practical deployment. By integrating IoT sensing with fuzzy reasoning, the proposed system shifts nutritional monitoring from retrospective assessment toward proactive dietary intervention. This work highlights the potential of intelligent nutrition technologies to enhance decision-making in community-based stunting prevention programs and provides a scalable foundation for preventive digital health applications.
Volume: 15
Issue: 2
Page: 1194-1201
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

Enhancing imbalanced dataset diagnosis using class-based input image composition

10.11591/ijai.v15.i2.pp1613-1622
Azzeddine Hlali , Majid Ben Yakhlef , Soulaiman El Hazzat
Deep learning models often falter when faced with small, imbalanced datasets or degraded image quality, leading to unacceptably high false prediction rates. To bridge this gap, we introduce class-based image composition. This technique reformulates training inputs by fusing multiple intra-class images into unique composite input images (CoImg). By concentrating information density and amplifying intra-class variance, CoImg forces the model to discern subtle, nuanced disease patterns that might otherwise be lost. We validated this approach using the optical coherence tomography dataset for image-based deep learning methods (OCTDL), a collection of seven imbalanced retinal disease scan categories. From this, we engineered Co-OCTDL: a perfectly balanced variant where each training sample exists as a 3×1 layout composite. To measure the impact of this new representation, we benchmarked the original dataset against its composite counterpart using a VGG16 architecture. Precision was paramount. We maintained identical hyperparameters and model structures across all experiments to ensure a rigorous, fair comparison. The results were transformative. While baseline datasets struggled, the enhanced Co OCTDL achieved a near-perfect F1-score of 0.995 and an AUC of 0.9996. The method effectively neutralized the risks of class imbalance. It didn't just improve the numbers; it refined the diagnostic reliability of the model.
Volume: 15
Issue: 2
Page: 1613-1622
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

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

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

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

Novel convolution neural network model for dysgraphia affected handwriting classification

10.11591/ijai.v15.i2.pp1418-1427
Nisha Ameya Vanjari , Prasanna J. Shete
It is estimated that 10% of the population in the world suffers from learning disabilities like dyslexia, dysgraphia, and dyscalculia. Learning disabilities are neurological disorders in which children struggle with reading, writing and mathematical skills. Dysgraphia disorder impacts on writing abilities of students and thus may be a hurdle in their learning and evaluation of subject matter. Hence early detection/prediction of learning disability (LD) in school going children will greatly help in providing necessary accommodations so as to ease their future learning curve. In recent years researchers have used several deep learning algorithms that produce automated and trained models which can be useful in the handwriting classification. To properly capture the distinct handwriting inconsistencies linked to dysgraphia, this study contains experiments that determine how various convolution neural network (CNN) model layers contribute to performance. To address it, this research focused on the improved novel model based on CNN and targeted dysgraphia English handwriting classification with 98% accuracy with 102,691 trainable parameters. The model is trained on both normal and dysgraphia-affected handwriting, increasing its accuracy in identifying individual differences.
Volume: 15
Issue: 2
Page: 1418-1427
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

An efficient approach for cyber-attack detection by using machine learning and deep learning algorithms

10.11591/ijai.v15.i2.pp1219-1235
Yasir Hussein Shakir , Mahmoud Mohamed Abdelhamied , Eshaq Aziz Awadh AL Mandhari , Ali Alkhazraji , Naglaa M. Reda
Cybercrime gained traction in the late 20th century. The capabilities of cyber-attackers have improved dramatically. One of the biggest challenges facing cybersecurity developers is safeguarding consumers' security and privacy. Interest in using AI approaches in cybersecurity has grown significantly because of the incredible proficiency these techniques have demonstrated across all domains. Even while machine learning algorithms are very effective at identifying malicious activity, there are still certain issues that lower performance accuracy. This paper has the novelty of deploying the Artificial Bee Colony (ABC) meta-heuristic algorithm with the K-Nearest Neighbors (KNN) classifier to detect cyber-attacks. It proposes a variant approach called KNN+Bee that detects attacks efficiently, achieving 99.86% overall accuracy. The NSL-KDD dataset of cyberattacks has been leveraged in the training and testing phases. The proposed approach has been contrasted with the most popular machine learning. According to experimental findings, the suggested model delves deeper into the identification of cyberattacks. It achieves unprecedented performance, outperforming other models in terms of precision, Recall, F-score and MCC. Furthermore, popular deep learning models have been implemented and examined on the same dataset. Results prove that GRU is the most accurate, reaching 99.71%.
Volume: 15
Issue: 2
Page: 1219-1235
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

Taxonomy of cooperative adaptation level for cooperative adaptive mobile applications

10.12928/telkomnika.v24i2.27542
Berhanyikun Amanuel; Addis Ababa University Gebreselassie , Nuno M.; University of Lisbon Garcia , Dida; Addis Ababa University Midekso
Adaptive mobile applications (AMAs) are software systems designed to dynamically adjust their behavior in response to contextual changes. When multiple AMAs coexist on the same device, they create an ecosystem of heterogeneous applications with distinct functionalities, interaction models, and sensor requirements. This diversity enables opportunities for cooperative adaptation, where applications synchronize their behavior for collective benefit. Building on prior work that identified cooperation as a key dimension of adaptive mobile systems, this study proposes a refined taxonomy of cooperation levels for AMAs. The taxonomy is validated through case studies and formal specification methods, demonstrating its theoretical soundness and practical applicability. The findings advance the understanding of cooperative adaptation mechanisms and provide structured guidance for designing and classifying cooperative AMAs.
Volume: 24
Issue: 2
Page: 500-513
Publish at: 2026-04-01
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