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31,458 Article Results

A machine learning framework for skin cancer classification using texture descriptors

10.11591/ijai.v15.i4.pp3556-3567
Shikha Malik , Vaibhav V. Dixit
Skin cancer remains a critical health and economic concern worldwide. Timely and accurate diagnosis is crucial for improving mortality rate of patients. Although automated machine learning (ML) models assist doctors in clinical assessment of skin cancer from dermoscopic images, their performance often suffers from extreme class imbalance, as benign image samples greatly exceed malignant ones. This leads to false detection and delayed diagnosis of skin cancer. To overcome this issue, the study proposes an efficient and lightweight geometric transformation (GT)–augmented support vector machine (SVM) framework for early diagnosis of skin cancer. It effectively addresses the class imbalance issues present in the datasets and improves the detection of positive cases. The novel pipeline integrates preprocessing, morphology preserving GT, rotation-invariant texture feature extraction, feature validation, and feature scaling for performing binary classification using an optimized SVM framework. This framework provides balanced and accurate lesion classification even if image samples are insufficient. Experimental results have successfully achieved a true positive rate (TPR) of 93% on PH2 and 81.1% on International Skin Imaging Collaboration 2016 (ISIC-2016) dataset, which is better than conventional ML models. These findings prove that proposed GT-SVM framework is a lightweight, interpretable, and computationally efficient approach for early skin cancer diagnosis. Future developments shall explore multi-class lesion classification, validation across diverse clinical datasets, and hybrid feature fusion.
Volume: 15
Issue: 4
Page: 3556-3567
Publish at: 2026-08-01

Experimental evaluation of an artificial intelligence system for automated mango classification

10.11591/ijai.v15.i4.pp3919-3933
Surasit Phokha , Wiriya Dangton , Viroch Sukontanakarn , Phisit Srinoi
This study proposes the design and implementation of an intelligent mango sorting system capable of classifying mangoes into three categories: unripe, ripe, and rotten. The system integrates a programmable logic controller (PLC) to operate a conveyor mechanism, with user interaction facilitated through a human-machine interface (HMI) touchscreen panel. Image acquisition is performed using a universal serial bus (USB) webcam, while image processing and classification are handled by the CiRA CORE software utilizing artificial intelligence (AI) techniques. The classification results are transmitted to an Arduino microcontroller, which controls pneumatic actuators responsible for the physical sorting process. Experimental results demonstrate that the system can accurately classify mangoes with an overall success rate of 90%, indicating its potential for practical application in automated agricultural product sorting.
Volume: 15
Issue: 4
Page: 3919-3933
Publish at: 2026-08-01

Resource optimization via identification of articles with aspects of priority and high utility

10.11591/ijai.v15.i4.pp3240-3251
Garima Srivastava , Vaishali Singh , Sachin Kumar
Owing to the linear economic growth, the manufacturing of consumer products has increased manifold, products in diversified packaging and formats are now available in abundance, sometimes even with less or no requirement, resulting in losses. Addressing the gap that exists between productivity and requirements can minimize losses and unnecessary burden on manufacturing units. To minimize losses, a hybrid algorithm is proposed, using the advantages offered by classification and high utility patterns to identify the dominant aspects of articles with a high probability of sale. Articles are identified in a two-step process, identification of samples with context as prominent parameter by vanilla feedforward neural network (VNN) as a first step. The second step comprises the determination of utility pattern mining in articles using faster high-utility itemset miner (FHN), sentiment score obtained provides the utility pattern of the product. Proposed hybrid VNN + FHN, along with convolutional neural network (CNN), long short-term memory (LSTM), and transformer-based prediction model (TPM) were used for assessing the utility-driven product analysis. The hybrid VNN + FHN proposed displays the best adaptability by extracting 158 patterns with a utility of 162.88 units. The algorithm outperforms CNN in utility, LSTM and TPM in speed, and CNN in pattern count, making it a better choice for resource optimization.
Volume: 15
Issue: 4
Page: 3240-3251
Publish at: 2026-08-01

WVisionBERT-VL: a multimodal model architecture for toxicity classification on social media platforms using large language models

10.11591/ijai.v15.i4.pp3365-3375
Witta Listiya Ningrum , Achmad Benny Mutiara , Diana Ikasari
The increasing prevalence of toxic content on social media, conveyed through text, images, and videos, poses significant challenges for automated content moderation systems. Although prior studies have reported promising results in unimodal and bimodal settings, they often fail to capture implicit and contextual toxicity emerging from interactions across multiple modalities, particularly in non-English environments. This paper proposes WVisionBERT-VL, an end-to-end multimodal framework for toxicity detection that integrates text, image, and video modalities within a unified architecture. The proposed model incorporates modality-specific encoders, bidirectional multi-head cross attention (BMHCA) for cross-modal synchronization, and an adaptive fusion gate to dynamically balance modality contributions. A balanced multimodal dataset is constructed from social media platforms, including X, Instagram, and TikTok, and refined using a model-based labeling strategy with limited human-in-the-loop validation. Experimental results on a custom Indonesian dataset demonstrate strong in-domain performance, achieving an accuracy of 94.12%, a macro-F1 of 0.9407, and a receiver operating characteristic - area under the curve (ROC-AUC) of 0.9721, with robustness further validated through five-fold cross-validation. Cross-dataset evaluation highlights challenges related to domain shift, underscoring the need for future research on robust and domain-adaptive multimodal toxicity detection.
Volume: 15
Issue: 4
Page: 3365-3375
Publish at: 2026-08-01

Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems

10.11591/ijai.v15.i4.pp3518-3527
Thang C. Vu , Dung T. Nguyen , Minh T. Nguyen , Long Q. Dinh , Mui D. Nguyen
This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
Volume: 15
Issue: 4
Page: 3518-3527
Publish at: 2026-08-01

A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network

10.11591/ijai.v15.i4.pp3903-3918
Ahmad Zarkasi , Hadipurnawan Satria , Anggina Primanita , Deris Stiawan
This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Volume: 15
Issue: 4
Page: 3903-3918
Publish at: 2026-08-01

Analysis of normalization technique on multi objective preference analysis method

10.11591/ijai.v15.i4.pp3827-3835
Fristi Riandari , Gabriel Ardi Hutagalung , Ferry Fachrizal
Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Volume: 15
Issue: 4
Page: 3827-3835
Publish at: 2026-08-01

Forecasting income inequality in Vietnam’s regions using machine learning

10.11591/ijai.v15.i4.pp3464-3475
T. Thai-Phuong , L. Nguyen-Son
This study applies artificial neural networks (ANN) and support vector regression (SVR) to forecast the Gini coefficient across Vietnam’s seven socio-economic regions using limited data from 2014–2022 (N = 63). Despite the small dataset, robust techniques including repeated cross-validation, temporal backtesting, regularization, and bootstrapping were employed to mitigate overfitting. The results show a modest continued decline in income inequality through 2025, with faster improvements in urban regions (Red River Delta and Southeast) than in rural and highland areas. ANN outperformed SVR with an average cross-validation coefficient of determination (R²) of 0.93. The novelty lies in its granular regional forecasting and region-specific policy recommendations beyond national-level analyses. Key drivers—per capita income and simple housing—support targeted interventions such as housing credits in the Mekong River Delta and agricultural investments in the Central Highlands. The findings provide an evidence-based foundation for regionally tailored strategies to reduce income disparities in Vietnam.
Volume: 15
Issue: 4
Page: 3464-3475
Publish at: 2026-08-01

Enhancing pedestrian detection in adverse conditions using YOLOv5s with adaptive weighted fusion efficient channel attention module

10.11591/ijai.v15.i4.pp3299-3308
Oumayma Rachidi , Badr Bououlid Idrissi , Chafik Ed-dahmani
Pedestrian detection constitutes a critical task within advanced driver assistance systems (ADAS), where reliable identification of pedestrians is essential for ensuring vehicular safety. Although deep learning has substantially improved detection performance, existing state-of-the-art models continue to exhibit notable degradation in adverse weather conditions and low-light. To mitigate these challenges, this study introduces an enhanced pedestrian detection framework based on you only look one version 5 (YOLOv5s), retrained on an augmented common object in context (COCO) dataset focused on the person class. Additionally, a novel, lightweight, and adaptive attention mechanism called: the weighted fusion efficient channel attention (WF-ECA) module is incorporated into the detection architecture. The WF-ECA module selectively focusses on important features without compromising computational efficiency or inference speed. Comparative experiments demonstrate a 5% increase in mean average precision (mAP) in comparison to the baseline model, thereby demonstrating the efficacy of the proposed attention module in improving detection robustness under challenging environmental conditions. These findings highlight the potential of attention-based mechanisms to enhance pedestrian detection performance in real-world ADAS applications.
Volume: 15
Issue: 4
Page: 3299-3308
Publish at: 2026-08-01

Hybrid deep learning model for the task scheduling in cloud computing

10.11591/ijai.v15.i4.pp3683-3691
Kavita Rani , Om Prakash Sangwan , Ritu Garg
Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Volume: 15
Issue: 4
Page: 3683-3691
Publish at: 2026-08-01

YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs

10.11591/ijai.v15.i4.pp3591-3602
Sri Oktamuliani , Luqyana Mahdiyah , Haritsul Haq , Nesa Perdana Putri , Wulandani Liza Putri
Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Volume: 15
Issue: 4
Page: 3591-3602
Publish at: 2026-08-01

Comparative performance analysis of lightweight face identification algorithm

10.11591/ijece.v16i4.pp2042-2060
Wuyun Wang , Suchada Sitjongsataporn
With the wide application of face recognition in resource-constrained scenarios like mobile and embedded devices, lightweight algorithms have become a research focus, but existing studies lack multi-dimensional, scenario-based performance comparisons. This paper studies the performance evaluation and application adaptation of lightweight face recognition algorithms, innovatively builds a scenario-based evaluation system, verifies the performance improvement of combining traditional algorithms with MobileNet, and constructs an efficient, stable and low-cost system. It elaborates on face recognition principles, including key links of face detection, feature extraction and matching, introduces traditional algorithms such as Eigenfaces, Fisherfaces and LBPH, and focuses on MobileNet’s characteristics: reducing computation and parameters via depthwise separable convolution, and adjustable width and resolution. Four comparative experiments verify the "traditional algorithms + MobileNet" hybrid strategy. Results show the combination achieves 98.1% accuracy, 4.3 percentage points higher than single MobileNet; LBPH + MobileNet balances performance and resource consumption best, with 110MB memory, 40% CPU usage and 315ms processing time. The hybrid strategy improves accuracy and efficiency in different scenarios, aiming to provide a scientific basis for the engineering application and subsequent optimization of lightweight face recognition algorithms, and supporting algorithm selection and performance improvement in resource-constrained scenarios.
Volume: 16
Issue: 4
Page: 2042-2060
Publish at: 2026-08-01

Image-based estimation of surface roughness in Al-7075 drilling

10.11591/ijai.v15.i4.pp3492-3504
Shilpa M. Karegoudra , Vamsidhar Yendapalli
Surface roughness is an important quality parameter that affects the performance, durability, and reliability of machined components. Measuring the internal surface roughness of drilled holes using conventional contact-based methods is often difficult. Accessibility of the internal surface is complicated due to the diameter of the drilled hole and the need to interrupt the machining process. To overcome these limitations, the proposed work offers a non-contact method for estimating the surface roughness of drilled Al-7075 using image-based analysis. Drilled surfaces are machined using different speeds and feed rates. High-resolution images of the drilled surface are captured using a custom-built image-capturing setup. Texture and frequency features were extracted using gray-level co-occurrence matrix (GLCM), discrete Fourier transform (DFT), and discrete wavelet transform (DWT) techniques. Surface arithmetic average roughness (Ra) was measured using these extracted features. The estimated roughness parameters were then validated by comparing them against roughness parameters obtained by means of the contact stylus technique. According to the experimental results, the accuracy of the wavelet technique is higher, with mean absolute errors of 0.32 μm compared to those obtained using GLCM and DFT techniques. The findings demonstrate that the proposed image-based framework is a reliable and practical solution for non destructive surface roughness prediction.
Volume: 15
Issue: 4
Page: 3492-3504
Publish at: 2026-08-01

Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences

10.11591/ijai.v15.i4.pp3189-3201
Felza Ridho , Mohammad Isa Irawan , Nurul Hidayat , Awik Puji Dyah Nurhayati
This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Volume: 15
Issue: 4
Page: 3189-3201
Publish at: 2026-08-01

Predictive model based on machine learning to identify sleep-related health problems

10.11591/ijai.v15.i4.pp3888-3902
Laberiano Andrade-Arenas , Inoc Rubio Paucar , Margarita Giraldo Retuerto , Cesar Yactayo-Arias
Sleep quality has become a growing public health issue worldwide, mainly due to a lack of awareness about its long-term consequences. Despite existing strategies to address this problem, there remains a need for more effective approaches. In this study, an early detection model for sleep disorders was implemented using the extreme gradient boosting (XGBoost) algorithm, following the knowledge discovery in databases (KDD) methodology, which includes the phases of selection, preprocessing, transformation, data mining, and interpretation. A dataset extracted from the Kaggle platform in CSV format was used, consisting of 374 records. With an overall accuracy of 91.5%, a recall of 100% for the insomnia class, and a precision of 100% for sleep apnea, the proposed model demonstrated exceptional performance. It also received an area under the curve (AUC) of 0.909 and an average F1-score of 0.913. With a mean accuracy of 91%, a 95% confidence interval (0.89–0.94), and a p-value of 0.0012, cross-validation confirmed its robustness and showed a statistically significant change from the baseline model. The error rates remained within clinically acceptable ranges, confirming its applicability as a diagnostic support tool. Overall, the results demonstrate the effectiveness of the model in identifying patterns related to sleep disorders.
Volume: 15
Issue: 4
Page: 3888-3902
Publish at: 2026-08-01
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