IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[Accelerating solder joint classification using generative artificial intelligence for data augmentation ]]>
<![CDATA[Ong, Teng Yeow]]>;
<![CDATA[Teoh, Chow Teoh]]>;
<![CDATA[Tan, Koon Tatt]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4382-4389
<![CDATA[Despite advancements in computer vision, deploying deep learning algorithms for automated optical inspection (AOI) in printed circuit board (PCB) manufacturing remains challenging due to the need for large, diverse, and high-quality training datasets. AOI programs must be developed quickly, often as soon as the first PCB is assembled, to meet tight production timelines. However, deep learning models require extensive datasets of defect images, which are both scarce and time-consuming to collect. As a result, AOI software developers frequently resort to traditional rule-based methods. This study introduces a novel framework that leverages generative AI and discriminative AI to address dataset limitations. By applying a diffusion model to systematically add and remove Gaussian noise, the framework generates realistic defect images, expanding the available training data. This data augmentation accelerates the learning process of deep learning models, enhancing their robustness and generalizability. Experimental results demonstrate that this approach improves AOI system performance by producing balanced datasets across various defect classes. The framework shortens training times while maintaining high inspection accuracy, facilitating faster deployment of AOI systems in manufacturing. This advancement enhances quality control processes, contributing to more efficient, and reliable mass production of PCBs.]]>
<![CDATA[An algorithm for controlling the transmission of video streams in a flying ad hoc network ]]>
<![CDATA[M. Alghazali, Salah M.]]>;
<![CDATA[Aljeazna, Wisam K. Mahdloom]]>;
<![CDATA[Rasol, Murtadha N.]]>;
<![CDATA[Polshchykov, Konstantin A.]]>;
<![CDATA[V. Likhosherstov, Rodion]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4290-4298
<![CDATA[This article discussing the enhancement of video surveillance in various territories through the implementation of a flying ad hoc network (FANET). The primary objective of the surveillance is for search and rescue operations. To optimize the quality of FANET video broadcasting, a decision-making algorithm for video stream management is introduced. This algorithm evaluates the likelihood of achieving high-quality video transmission. Depending on the assessed probabilities, the algorithm recommends one of the following actions: initiating a new video stream transmission, reducing the average length of wireless channels, or discontinuing the transmission of low-information video streams. Computational experiments demonstrate a significant improvement in the accuracy of decision-making regarding the management of video stream transmission to FANET when utilizing the proposed algorithm.]]>
<![CDATA[Advanced risk assessment using machine learning and sentiment analysis on log data ]]>
<![CDATA[Turab, Nidal]]>;
<![CDATA[Abushattal, Abdelrahman]]>;
<![CDATA[Al-Nabulsi, Jamal]]>;
<![CDATA[Owida, Hamza Abu]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3897-3905
<![CDATA[Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.]]>
<![CDATA[Hybridized deep learning model with novel recommender for predicting criticality state of patient using MIMIC-IV dataset ]]>
<![CDATA[Khope, Sarika]]>;
<![CDATA[Kotambkar, Deepali]]>;
<![CDATA[Adiraju, Rama Vasantha]]>;
<![CDATA[Battalwar, Smita Suhas]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3926-3933
<![CDATA[The contribution of machine learning towards prediction of critical state of patient is the prime focus of the current study. The review of current approaches of machine learning has been witnessed with various shortcomings. Hence, the proposed study adopts medical information mart for intensive care (MIMIC-IV) dataset in order to develop a novel analytical model that can predict the criticality state of patient in their next visit. The model has been designed by hybridizing convolution neural network (CNN) and long short-term memory (LSTM) which takes the discrete input of hospital and individual patient information in each visit. The concatenated feature is then subjected to a newly introduced recommender module which offers implicit feedback by assigning a ranking score. The final predictive outcome of study offers criticality rank. The study model is benchmarked with existing machine learning approaches to find 54% of increased accuracy and 70% of reduced processing time.]]>
<![CDATA[Early goat disease detection using temperature models: k-nearest neighbor, decision tree, naive Bayes, and random forest ]]>
<![CDATA[Putra, Fareza Ananda]]>;
<![CDATA[Wella, Wella]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3835-3846
<![CDATA[This study aims to aid livestock activities by enabling early detection of diseases in goats through body temperature measurement. Early detection is crucial to prevent disease spread and improve livestock welfare. Using the knowledge discovery in databases (KDD) methodology, the study involves collecting, processing, and analyzing goat body temperature data. Four algorithms—k-nearest neighbor (KNN), decision tree, naive Bayes, and random forest—were used to develop disease detection models. The decision tree algorithm was found to be the most accurate, achieving 100% accuracy. This demonstrates its effectiveness in detecting diseases based on body temperature. Implementing this model is expected to significantly benefit farmers by helping maintain the health and productivity of their livestock.]]>
<![CDATA[Pre-trained convolutional neural network-based algorithms: application for recognizing the age category ]]>
<![CDATA[Yamasari, Yuni]]>;
<![CDATA[Anggraini, Lusiana]]>;
<![CDATA[Qoiriah, Anita]]>;
<![CDATA[Eka Putra, Ricky]]>;
<![CDATA[Agustin Tjahyaningtijas, Hapsari Peni]]>;
<![CDATA[Ahmad, Tohari]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3576-3587
<![CDATA[Cybercrime is a major issue in the current digital era, with one of its branches-cyber pornography-notably affecting Indonesia. Various efforts have been made to suppress or prevent this problem. One alternative solution involves using technological advances to recognize age ranges based on facial recognition. This age range recognition can be implemented to prevent users from accessing content that is not appropriate for their age. An optimal age-range recognition system is essential for this purpose. However, limited research has focused on this domain. Therefore, our research aimed to develop the best possible system. The proposed method applies a trained convolutional neural network (CNN) as a feature extractor to the artificial neural network (ANN) and k-nearest neighbor (K-NN) methods for age recognition based on facial images. By incorporating computational learning techniques, the system's performance is significantly enhanced, leveraging advanced algorithms to improve accuracy. The test results show that the performance of the pre-trained CNN-based ANN model is superior. This is indicated by the model's accuracy and F1-score, which were 11% and 0.11 higher, than the pre-trained CNN-based K-NN model. The error rate of the pre-trained CNN-based ANN model was also reduced by 0.11.]]>
<![CDATA[An algorithm for training neural networks with L1 regularization ]]>
<![CDATA[Gribanova, Ekaterina]]>;
<![CDATA[Gerasimov, Roman]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3781-3789
<![CDATA[This paper presents a new algorithm for building neural network models that automatically selects the most important features and parameters while improving prediction accuracy. Traditional neural networks often use all available input parameters, leading to complex models that are slow to train and prone to overfitting. The proposed algorithm addresses this challenge by automatically identifying and retaining only the most significant parameters during training, resulting in simpler, faster, and more accurate models. We demonstrate the practical benefits of the proposed algorithm through two real-world applications: stock market forecasting using the Wilshire index and business profitability prediction based on company financial data. The results show significant improvements over conventional methods: models use fewer parameters–creating simpler, more interpretable solutions–achieve better prediction accuracy, and require less training time. These advantages make the algorithm particularly valuable for business applications where model simplicity, speed, and accuracy are crucial. The method is especially beneficial for organizations with limited computational resources or that require fast model deployment. By automatically selecting the most relevant features, it reduces the need for manual feature engineering and helps practitioners build more efficient predictive models without requiring deep technical expertise in neural network optimization.]]>
<![CDATA[A hybrid steganography scheme with reduced difference expansion and pixel-value ordering ]]>
<![CDATA[Putra, I Kadek Agus Ariesta]]>;
<![CDATA[Croix, Ntivuguruzwa Jean De La]]>;
<![CDATA[Ahmad, Tohari]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3563-3575
<![CDATA[Steganography embeds secret messages into public media while ensuring the stego content remains visually indistinguishable from the original. The primary challenge lies in maximizing embedding capacity and image quality without introducing noticeable distortions. This research proposes a novel reversible data hiding (RDH) scheme that integrates reduced difference expansion (RDE) with four directional pixel-value ordering (PVO) schemes, horizontal, vertical, diagonal-right, and diagonal-left, to enhance embedding efficiency and visual fidelity. Unlike existing RDH methods that apply RDE with fixed or limited PVO directions, the proposed scheme dynamically selects the optimal PVO orientation based on pixel pair characteristics, effectively improving local prediction accuracy and reducing embedding-induced distortion. Previous studies have largely overlooked this relationship between pixel pair selection and embedding performance. Experimental evaluation on medical images with secret data sizes ranging from 5 kb to 100 kb demonstrates significant gains over recent PVO-based methods. The proposed method increases the average embedding capacity from 0.8315 to 0.9781 bit per pixel (bpp) (a 17.6% improvement) and raises the average peak signal-to-noise ratio (PSNR) from 49.44 to 53.40 dB, reducing distortion by approximately 3.96 dB.]]>
<![CDATA[Building change detection via classification in high-resolution aerial imagery ]]>
<![CDATA[Merza, Hayder Mosa]]>;
<![CDATA[Sbeity, Ihab]]>;
<![CDATA[Dbouk, Mohamed]]>;
<![CDATA[Ibrahim, Zein Al-Abidin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4319-4331
<![CDATA[This research investigates the detection of changes in building structures within high-resolution aerial images of Baghdad, Iraq, over two years, 2007 and 2024. Employing advanced remote sensing techniques and sophisticated image processing algorithms, this study aims to identify and quantify alterations in the urban landscape accurately by addressing the key challenges inherent in the image registration process, as well as the availability associated with change detection (CD) techniques. We examined the data collection strategies, evaluated matching methods, and compared CD approaches. Aerial images were accurately analyzed to detect changes in building footprints, construction activities, and destruction. We developed a comprehensive annotation methodology tailored to the complex urban environment of Baghdad. These findings emphasize the rapidly evolving nature of Baghdad’s urban fabric and the critical need for ongoing monitoring to inform urban planning and management strategies. The results demonstrate the efficacy of utilizing high-resolution aerial imagery with object-based CD techniques for detailed urban analysis. This research advances the existing knowledge by providing a robust framework for urban CD, with implications for enhancing urban planning and policy-making processes. Future research will focus on refining the annotation processes and incorporating additional data sources to enhance the accuracy and comprehensiveness of urban CD methodologies.]]>
<![CDATA[Residual edge dense enhanced module network: a deep learning approach with multi-class SVM for lung tumor stage classification ]]>
<![CDATA[Jayaraman, Prabakaran]]>;
<![CDATA[Selvaraj, Pandiaraj]]>;
<![CDATA[Elango, Ashwini]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4032-4042
<![CDATA[Lung cancer segmentation with positron emission tomography (PET) and computed tomography (CT) images plays a critical role to accurately detect lung cancer. Nevertheless, lung tumor segmentation in PET/CT images were extremely difficult due to the movement caused by respiration. Despite this fact, the lung tumor images shown large number of variations mostly in PET images and CT images. As PET-CT images are acquired concurrently the shape and size of lung tumor varies according to modality. To address these issues, we developed a residual edge dense enhanced module network (REDEM-NET) framework for lung tumor stage classification. The proposed REDEM-NET can process PET and CT images as inputs. In addition, the dense residual convolutional network (DRCN) collects both inputs and extracts high-dimensional features concurrently. The extracted features from both imaging modalities were fed into UNet+++ to obtain multi-level decoded features. The extracted decoded features are concurrently supplied to the pixel level learning module (PELM) and edge level learning module (E2LM) which resulting in two outputs for subsequent learning. The outputs were merged to provide a very precise lung tumor segmentation. Furthermore, segmented tumor was fed to multi-class support vector machine (MC-SVM) for lung tumor stage classification. Moreover, it was able to identify three stages and its substages namely primary tumor, region lymph node and distant metastasis.]]>
