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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 2,057 Documents
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Image-based estimation of surface roughness in Al-7075 drilling Shilpa M. Karegoudra; Vamsidhar Yendapalli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3492-3504

Abstract

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.
Predictive model based on machine learning to identify sleep-related health problems Laberiano Andrade-Arenas; Inoc Rubio Paucar; Margarita Giraldo Retuerto; Cesar Yactayo-Arias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3888-3902

Abstract

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.
WVisionBERT-VL: a multimodal model architecture for toxicity classification on social media platforms using large language models Witta Listiya Ningrum; Achmad Benny Mutiara; Diana Ikasari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3365-3375

Abstract

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.
Resource optimization via identification of articles with aspects of priority and high utility Garima Srivastava; Vaishali Singh; Sachin Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3240-3251

Abstract

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.
A machine learning framework for skin cancer classification using texture descriptors Shikha Malik; Vaibhav V. Dixit
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3556-3567

Abstract

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.
E-TEXTLOC: efficient text localization and extraction in real-world video scenes Dayananda Kodala Jayaram; Puttegowda Devegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3537-3545

Abstract

Localization of an accurate text is quite a complicated issue, especially when attempting to extract from a complex video. It is mainly due to the limitation of resources, dynamic background, and text variability. There is various artificial intelligence based methods adopting machine learning for addressing such issues encounter lower positional accuracy and incur maximized computational cost. Therefore, the proposed system introduces efficient text localization and extraction for comprehensive positional accuracy in complex videos (E-TEXTLOC). Different from conventional approaches, E-TEXTLOC facilitates sampling of video frames while MobileNetV2 is deployed towards faster localization of text. The outcome of recognized text is further refined by a verifier module, which provides a self-supervised response. Assessed on the YouTube video dataset, the proposed model accomplishes 98.2% accuracy with 29.6 ms towards generating analytical outcomes. It means the proposed model accomplishes 6-10% accuracy enhancement with a 40-50% reduction of speed in contrast to the existing system. The implications of the proposed study can be stated towards surveillance system, autonomous vehicles, and assistive devices that works in real-time.
Deep learning for lung cancer diagnosis: a comparative artificial intelligence study Phaneendra Varma Chintalapati; Prasanth Aruchamy; Alur Praneetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3120-3130

Abstract

Early and accurate lung cancer diagnosis (LCD) is critical, yet traditional imaging methods such as X-rays and computed tomography (CT) scans are costly, invasive, and heavily reliant on expert interpretation. This study investigates artificial intelligence (AI)-driven diagnostics by comparing deep learning models (convolutional neural network (CNN), residual network (ResNet), and visual geometry group 16 (VGG16)) with traditional machine learning algorithms (logistic regression (LR) and support vector machine (SVM)), using lung image database consortium and image database resource initiative (LIDC-IDRI) and Kaggle lung CT scan datasets. Performance was evaluated across multiple metrics: accuracy, sensitivity, specificity, and area under the curve-receiver operating characteristic (AUC-ROC). Among the models, ResNet achieved the highest performance, with an accuracy of 94%, sensitivity of 95%, specificity of 93%, and AUC-ROC close to 1. CNN and VGG16 also showed superior metrics compared to LR and SVM, highlighting the robustness of deep learning techniques. These results demonstrate that deep learning models not only achieve higher diagnostic accuracy but also significantly reduce false detections compared to traditional approaches. The findings support the potential of AI to automate and enhance LCD, thereby improving accessibility, consistency, and speed in clinical settings. Future work will emphasize clinical validation, address ethical challenges, and focus on integrating AI models into real-world healthcare workflows to improve patient outcomes.
Comparison of machine learning algorithms to identify muscular atrophy and muscular dystrophy Ganga Bhavani Billa; Venkateswara Rao Peddada
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3505-3517

Abstract

Muscular dystrophy is a genetic disorder characterized by progressive muscle weakness and degeneration, while muscular atrophy involves the loss of muscle tissue due to pathological conditions. Accurate differentiation between these neuromuscular disorders is critical for effective clinical diagnosis and treatment planning. This study presents a machine learning– based framework for classifying muscular dystrophy and muscular atrophy using surface electromyography (sEMG) signals. sEMG data were acquired and processed to extract informative features with low computational complexity. Several machine learning classifiers several techniques were used and assessed, such as random forest (RF), support vector machine (SVM), gradient boosting (GB), and extreme gradient boosting (XGBoost). To further enhance classification performance, a novel ensemble learning approach combining multiple machine learning models was proposed. Experimental results exhibit that the suggested ensemble model completes significantly higher grouping accurateness and robustness compared to individual classifiers and existing methods reported in the literature. The results of this investigation indicate that ensemble-based machine learning techniques integrated with sEMG analysis provide a reliable and non-invasive diagnostic aid, with strong potential for early detection and improved clinical management of neuromuscular disorders.
Experimental evaluation of an artificial intelligence system for automated mango classification Surasit Phokha; Wiriya Dangton; Viroch Sukontanakarn; Phisit Srinoi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3919-3933

Abstract

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.
Integration of artificial intelligence technology in learning evaluation using the ADDIE method Adip Wahyudi; Slamet Arifin; Mukhammad Solikhin; Alfi Sahrina; Feri Fahrian Maulana; Mohammad Firzon Ainur Roziqin; Maria Emerita Indraningrum Shrestha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3068-3080

Abstract

Artificial intelligence offers significant potential to enhance learning evaluation, yet implementing fair and objective assessments remains a challenge. This study developed an artificial intelligence-based learning evaluation system to improve assessment inclusivity and efficiency. Employing the research and development method with the analysis, design, development, implementation, and evaluation (ADDIE) model, the system features a virtual assistant chatbot and an automated grading tool artificial intelligence assessment. The product underwent validation by media and material experts, followed by field trials with 20 secondary school teachers. The results demonstrated high feasibility, with validation scores of 96% by user interface/user experience (UI/UX) and 91.2% by material experts, and an implementation feasibility score of 94.4%. The system successfully assisted teachers in reducing administrative burdens and providing personalized feedback based on student learning styles. Future developments should expand subject coverage and support diverse assessment formats.

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