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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 1,974 Documents
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Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach Hengki Tamando Sihotang; Jonson Manurung; Bambang Saras Yulistiawan; Galih Prakoso Rizky A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2449-2458

Abstract

An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Adaptive multi-scale convolutional network for plant leaf disease detection and classification Tejashwini C. Gadag; D. R. Kumar Raja
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2956-2969

Abstract

Plant disease detection is a critical task in modern agriculture, directly impacting crop yield, food security, and sustainable farming practices. Traditional methods rely on expert visual inspection, which is time consuming, inconsistent, and inaccessible in remote areas. This study introduces advanced deep learning (DL) framework, the adaptive multi-scale convolutional network (AMS-ConvNet) optimized for accurate and efficient plant disease identification. Hierarchical feature extraction network (HFEN) integrates the multi-domain attention framework (MDAF) and adaptive scale fusion module (ASFM) to enhance feature extraction and address challenges such as complex natural backgrounds, non-uniform leaf structures, and varying environmental conditions. The proposed framework employs pre trained knowledge adaptation (PTKA) techniques to improve generalization and overcome data scarcity. Comprehensive evaluations on multiple datasets demonstrate the model's better performance, achieving state-of-the-art metrics in precision, recall, F1-score, and accuracy. Furthermore, this approach ensures scalability and adaptability, making it suitable for real field conditions. The study emphasizes the importance of robust, automated solutions in minimizing crop losses, reducing labor costs, and enhancing agricultural sustainability through precision disease management.
Adaptive synthetic-based arrhythmia classification using machine learning techniques Md. Rabiul Islam; Tapan Kumar Godder; Ahsan Ul-Ambia; Ferdib Al-Islam; Bulbul Ahamed; Anindya Nag; Ariful Islam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2398-2409

Abstract

Cardiac arrhythmias, characterized by irregular heart rhythms, pose a significant challenge for timely and accurate diagnosis. This paper presents an advanced framework for arrhythmia classification that addresses the class imbalance issue using Adaptive Synthetic (ADASYN) sampling combined with a comprehensive set of machine learning techniques. We implemented various classifiers, including Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron, Gradient Boosting, AdaBoost, Light Gradient Boosting, CatBoost, and Extreme Gradient Boosting. Our experimental results demonstrate that Random Forest, Gradient Boosting, Light Gradient Boosting, and XGBoost achieved a remarkable accuracy of 97%. Other models, such as Decision Tree and Logistic Regression, also performed well, achieving 95% and 94% accuracy, respectively. KNN and Naive Bayes yielded 93% and 81% accuracy, respectively, while AdaBoost underperformed with an accuracy of 24%. Precision scores across the models remained high, except for Naive Bayes and AdaBoost. All models, except AdaBoost, demonstrated excellent recall. Our proposed methodology outperforms previous works, setting a new benchmark for arrhythmia classification. These findings emphasize the effectiveness of integrating ADASYN with machine learning techniques to enhance arrhythmia detection, with significant potential for improving clinical diagnostic processes and patient outcomes.
Utilization of depth-wise and spatially separable convolutional network fusion for classification of white blood cells Firas Muneam Bachay; Ali Abbas Alzaheiree; Hassenien Ali Hussein; Ahmed Nooruldeen Alsafi; Mohammed Hasan Abdulameer
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2595-2605

Abstract

White blood cells (WBCs) are an essential part of the human immune system, playing a significant role in fighting diseases and infections. Their detection and classification from microscopic blood images is a crucial step in diagnosing various diseases. Looking at cells by hand is still key, but it takes a lot of work and mistakes can happen. So, this study tries to improve how to find and sort WBCs using some cool computer tricks. The study tackling issues like cells being on top of each other, looking different, and not having a ton of data. To achieve this, image enhancement techniques were applied using contrast enhancement algorithm, contrast-limited adaptive histogram equalization (CLAHE), and image segmentation techniques using color isolation are employed, which contributes to more accurate separation of overlapping cells, and enables faster and more efficient diagnosis. To efficiently complete the classification process after the segmentation process, a neural network structure consisting of combining three types of convolutional layers (depthwise, spatially, and convolution) was used. To evaluate the proposed technique, experiments were conducted using an open-source blood cell count and detection (BCCD) dataset from the Kaggle platform, and resulted in achieving a classification accuracy of 99.06% and an F1-score of 99.05%. This highlight of the model’s ability to efficiently deal with the challenges associated with WBC classification.
Artificial intelligence-based risk assessment in agro-industry using supervised neural networks Imam Santoso; Izzum Wafi'uddin; Naila Maulidina Lu'ayya; Annisa'u Choirun; Siti Asmaul Mustaniroh; Dodyk Pranowo; Ainur Rofiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2260-2268

Abstract

The coffee supply chain involves high production volumes, complex multi actor interactions, and increasing sustainability requirements, yet remains highly vulnerable to risks dimension. This study aims to develop and evaluate a decision-support framework that improves the accuracy and consistency of sustainability risk classification in the coffee supply chain. The proposed framework integrates failure mode and effect analysis (FMEA) with a supervised artificial neural network (ANN) using backpropagation (BP) to enable data-driven and adaptive risk assessment. Empirical data was collected from 55 respondents, resulting in the identification of 35 supply chain risk factors. These data were used to train and validate an ANN-based classification model implemented in a Python environment, with standard preprocessing and stratified data partitioning to ensure robustness. The ANN classified risks into five categories using supervised learning. The results demonstrate strong predictive performance, achieving overall accuracy of 98.97%, with precision, recall, and F1-scores exceeding 96.8% across all risk classes. Confusion matrix analysis confirms reliable generalization and minimal misclassification. The findings indicate that integrating FMEA with ANN-BP significantly enhances risk classification compared to conventional qualitative approaches. The proposed framework provides a scalable and reliable decision-support tool for dynamic risk scoring, supporting enhancement of sustainable practices in agro-industrial coffee supply chains.
Deep hybrid models for bitcoin forecasting: EMD, CEEMDAN,and LSTM in comparison Ayoub Aarabi; Maryem Ait Moulay; Issam Bouganssa; Abdelali Lasfar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2797-2810

Abstract

In this study, an artificial neural network (ANN) was developed to forecast Bitcoin prices using one of the most successful deep learning architectures for time series analysis: long short-term memory (LSTM) networks. This model was enhanced with a signal processing layer that reduces the impact of the instrument’s high volatility on prediction accuracy by applying two signal decomposition techniques: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). This study is motivated by the major fluctuations in Bitcoin prices, which make precise forecasting difficult but crucial for experts and investors. This findings demonstrate that forecasting performance improves when decomposition techniques are used. In particular, compared to the conventional LSTM and EMD-LSTM models, the CEEMDAN-LSTM model achieved the highest accuracy, with a mean absolute error (MAE) of 167.837 and a root mean square error (RMSE) of 255.673, outperforming both EMD-LSTM (MAE =168.785, RMSE =256.042) and the standard LSTM (MAE =169.516, RMSE=256.225). The combination of CEEMDAN and LSTM results in a more reliable model that can accurately capture short-term fluctuations in Bitcoin prices.
Image enhancement combined with EfficientNet-B7 for grading classification of diabetic retinopathy Nina Sevani; Edy Kristianto; Albert Salomo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2554-2566

Abstract

Diabetic retinopathy (DR) is a complication caused by poorly managed diabetes that affects the eyes. According to the World Health Organization (WHO), 422 million people worldwide have suffered from DR in the past ten years. Manual detection using retinal fundus images is time-consuming and requires experienced ophthalmologists. This study proposes a deep learning method using the pre-trained model EfficientNet-B7 to identify this disease automatically. Five levels of DR will be classified: no-DR, mild-DR, moderate-DR, severe-DR, and proliferative-DR. The model was trained using "APTOS 2019 blindness detection" dataset, and image augmentation was performed. Image segmentation techniques such as contrast limited adaptive histogram equalization (CLAHE) and real enhanced super resolution generative adversarial network (Real-ESRGAN) were applied during preprocessing to improve the model's accuracy significantly. The implementation of CLAHE resulted in the validation accuracy improvement from 76.6% to 83.4% compared to no segmentation, while the combination of Real-ESRGAN and CLAHE increased the accuracy to 93.7%. Future research can explore the combination of CLAHE with other image processing techniques apart from the Real-ESRGAN model.
Fine-tuning convolutional neural network for artificial intelligence generated image detection enhancement Steven Vincent Hendrawan; Moeljono Widjaja; Alethea Suryadibrata
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2238-2246

Abstract

The relationship between art and technology has changed how people engage with creativity, leading to the industrialization of the field. Various digital media have been utilized in the endeavour of art creation, such as artificial intelligence (AI) generation for images. The utilization of AI-generated art has yielded negative reactions due to its exploitative nature on pre-existing artworks without the creator’s consent, which raises plagiarism concerns. This research utilized convolutional neural network (CNN) to help detect such images to reduce public concerns on the abuse of AI images. The algorithm is proposed to detect such images as it involves spatial convolution within two-dimensional spaces, matching the nature of images. The model was developed from pre-existing architectures, namely EfficientNetB1 and Xception, which was pre-trained on ImageNet classification task with the modification of inclusion or exclusion of dropout in the top layer. After assessing the models, removing top layer dropout from EfficientNetB1 model improved it to reach the F1-score of 97.66% compared to 97.44% in the base model and Xception with a dropout layer yields lower F1-score of 95.56% compared to 97.07% in the base model.
Pneumothorax detection using a learning focal point architecture Salah-Eddine Mansour; Bouabid Qabliyane; Abdelhak Sakhi; Zakaria Khoudi; Mohamed Baslam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2041-2052

Abstract

Automatic image segmentation and feature analysis play a crucial role in improving the accuracy and efficiency of disease diagnosis and treatment within modern medical practice. This study propose the use of the learning focal point (LFP) architecture, which is based on the LFP algorithm, to perform effective segmentation of medical images by dividing each image in the dataset into multiple meaningful zones. This zonal segmentation strategy enables the precise extraction of critical regions of interest that are most relevant for pathological analysis. The proposed approach is specifically applied to the detection of common pneumothorax in lung imaging, a condition that requires timely and accurate diagnosis. By concentrating on essential lung zones, the LFP architecture enhances the reliability and robustness of pneumothorax identification. The results demonstrate that this method has the potential to significantly assist clinicians by providing more accurate diagnostic support and facilitating earlier medical intervention, ultimately improving patient outcomes.
Brain tumor detection using VGG-16 model Aicha Oussous; Abderrahmane Ez-zahout; Soumia Ziti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2337-2346

Abstract

Research in medical image analysis, specifically through deep convolutional networks, addresses the challenges of manually analyzing large magnetic resonance imaging (MRI) image volumes for brain tumor detection. The manual analysis is time-consuming, tedious, and prone to inaccuracies due to subtle visual similarities between normal tissue and tumor cells. This research aims to automate tumor detection, increasing accuracy and efficiency in medical treatments. This study aimed to develop a model capable of classifying brain tumors 2D MRI images, and the convolutional neural network (CNN)-based model successfully achieved an accuracy of 99.21% but suffered from noticeable Overfitting. Implementing the independent tests set and early stopping mitigated this issue, making the model more reliable for production deployment and demonstrating its potential in supporting physicians in detecting brain tumors, thereby enhancing treatment efficiency. The use of Python, TensorFlow, and Keras facilitated the development of the proposed solution, focusing on a diverse set of MRI images with varying tumor sizes, locations, shapes, and intensities.

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