cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 1,722 Documents
 146   147   148   149   150 
Transformer+transformer architecture for image captioning in Indonesian language Wijaya, Bryan Christofer; Sugiarto, Hendrik Santoso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2338-2346

Abstract

Image captioning in Indonesian language poses a significant challenge due to the complex interplay between visual and linguistic comprehension, as well as the scarcity of publicly available datasets. Despite considerable advancements in this field, research specifically targeting the Indonesian language remains scarce. In this paper, we propose a novel image captioning model employing a transformer-based architecture for both the encoder and decoder components. Our model is trained and evaluated on the pre-translated Flickr30k dataset in the Indonesian language. We conduct a comparative analysis of various transformertransformer configurations and convolutional neural network (CNN)-recurrent neural network (RNN) architectures. Our findings highlight the superior performance of a vision transformer (ViT) as the visual encoder, combined with IndoBERT as the textual decoder. This architecture achieved a BLEU-4 score of 0.223 and a ROUGE-L score of 0.472.
Uncertainty-aware contextual multi-armed bandits for recommendations in e-commerce Subramani, Anantharaman; Kumar, Niteesh; Chowdhury, Arpan Dutta; Prajapat, Ramgopal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2519-2527

Abstract

The growing e-commerce landscape has seen a shift towards personalized product recommendations, which play a critical role in influencing consumer behavior and driving revenue. This study explores the efficacy of contextual multi-armed bandits (CMAB) in optimizing personalized recommendations by intelligently balancing exploration and exploitation. Recognizing the inherent uncertainty in user behaviors, we propose an enhanced CMAB policy that incorporates item correlation matrix as an additional component of uncertainty to the conventional binary exploration and exploitation setup of bandit policies. Our approach aims to increase the overall relevance of recommendations through the 'triadic framework’ of CMAB, that seamlessly integrates with existing bandit policies, enabling adaptive recommendations based on diverse user attributes. By outperforming traditional models, this uncertainty-aware method demonstrates its potential in refining recommendation accuracy, thus maximizing revenue in a competitive e-commerce environment. Future research will explore dynamic uncertainty modeling and cross-domain applications to further advance the field.
Image analysis and machine learning techniques for accurate detection of common mango diseases in warm climates Rahib, Md Abdullah Al; Sultana, Naznin; Saha, Nirjhor; Mia, Raju; Sarkar, Monisha; Sattar, Abdus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2935-2944

Abstract

Mangoes are valuable crops grown in warm climates, but they often suffer from diseases that harm both the trees and the fruits. This paper proposes a new way to use machine learning to detect these diseases early in mango plants. We focused on common issues like mango fruit diseases, leaf diseases, powdery mildew, anthracnose/blossom blight, and dieback, which are particularly problematic in places like Bangladesh. Our method starts by improving the quality of images of mango plants and then extracting important features from these images. We use a technique called k-means clustering to divide the images into meaningful parts for analysis. After extracting ten key features, we tested various ways to classify the diseases. The random forest algorithm stood out, accurately identifying diseases with a 97.44% success rate. This research is crucial for Bangladesh, where mango farming is essential for the economy. By spotting diseases early, we can improve mango production, quality, and the livelihoods of farmers. This automated system offers a practical way to manage mango diseases in regions with similar climates.
Enhancing precision agriculture: a comprehensive investigation into pathogen detection and management Farhat, Shaista; Anuradha, Chokka
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3121-3132

Abstract

Agriculture is an important sector of Indian agronomy for human livelihood. All areas are affected by the effects of environmental toxic farms, which makes managing various difficult situations more challenging. Agriculture must adopt new technology in accordance with daily environmental changes if it is going to benefit from a crop from the perspectives of farmers and end users. Farmers will benefit from early detection of agricultural diseases rather than risking their lives in dangerous circumstances. Computer technology will be very helpful in maintaining sustainable and healthy crops for the objective of identifying crop diseases in addition to the farmer's close observation. Deep learning (DL) techniques are very influential among various computing technologies. In this work, we explore several current approaches to precision agriculture, such as artificial intelligence (AI), DL, and machine learning (ML). The findings of the study make clear modern methods, their drawbacks, and the knowledge lacking that needs to be addressed to explore precision agriculture fully.
Artificial intelligence predictive modeling for educational indicators using data profiling techniques Nai, Soukaina; Elbaghazaoui, Bahaa Eddine; Rifai, Amal; Sadiq, Abdelalim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3063-3073

Abstract

In Morocco, the escalating challenges in the education sector underscore the necessity for precise predictions and informed decision-making. Effective management of the education system depends on robust statistical data, which is crucial for guiding decisions, refining policies, and improving both the quality and accessibility of education. Reliable indicators are vital for ensuring efficiency, equity, and accuracy in educational planning and decision- making. Without dependable data, implementing effective policies, addressing the needs appropriately, and achieving positive outcomes becomes difficult. This paper aims to identify the optimal machine learning model for analyzing educational indicators by comparing a range of advanced models across a comprehensive set of metrics. The objective is to determine the most effective model for profiling relevant information and addressing predictive challenges with high accuracy.
Data-driven support vector regression-genetic algorithm model for predicting the diphtheria distribution Anggraeni, Wiwik; Sudiarti, Yeyen; Perdana, Muhammad Ilham; Riksakomara, Edwin; Sooai, Adri Gabriel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2909-2921

Abstract

Indonesia is one of the countries with the largest number of diphtheria sufferers in the world. Diphtheria is a case of re-emerging disease, especially in Indonesia. Diphtheria can be prevented by immunization. Diphtheria immunization has drastically reduced mortality and susceptibility to diphtheria, but it is still a significant childhood health problem. This study predicted the number of diphtheria patients in several regions using support vector regression (SVR) combined with the genetic algorithm (GA) for parameter optimization. The area is grouped into 3 clusters based on the number of cases. The proposed method is proven to overcome overfitting and avoid local optima. Model robustness tests were carried out in several other regions in each cluster. Based on the experiments in three scenarios and 12 areas, the hybrid model shows good forecasting results with an average mean squared error (MSE) of 0.036 and a symmetric mean absolute percentage error (SMAPE) of 41.2% with a standard deviation of 0.075 and 0.442, respectively. Based on experiments in various scenarios, the SVR-GA model shows better performance than others. Compares two- means tests on MSE and SMAPE were given to prove that SVR-GA models have better performance. The results of this forecasting can be used as a basis for policy-making to minimize the spread of diphtheria cases.
Leveraging machine learning for column generation in the dial-a-ride problem with driver preferences Ouasaid, Sana; Saddoune, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2826-2838

Abstract

The dial-a-ride problem (DARP) is a significant challenge in door-to-door transportation, requiring the development of feasible schedules for transportation requests while respecting various constraints. This paper addresses a variant of DARP with time windows and drivers’ preferences (DARPDP). We introduce a solution methodology integrating machine learning (ML) into a column generation (CG) algorithm framework. The problem is reformulated into a master problem and a pricing subproblem. Initially, a clustering-based approach generates the initial columns, followed by a customized ML-based heuristic to solve each pricing subproblem. Experimental results demonstrate the efficiency of our approach: it reduces the number of the new generated columns by up to 25%, accelerating the convergence of the CG algorithm. Furthermore, it achieves a solution cost gap of only 1.08% compared to the best-known solution for large instances, while significantly reducing computation time.
A comprehensive review of interpretable machine learning techniques for phishing attack detection Chandre, Pankaj Ramchandra; Bhujbal, Pallavi; Jadhav, Ashvini; Dinesh Shendkar, Bhagyashree; Wangikar, Aditi; Sachdeo, Rajneeshkaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3022-3032

Abstract

Phishing attacks remain a significant and evolving threat in the digital landscape, demanding continual advancements in detection methodologies. This paper emphasizes the importance of interpretable machine learning models to enhance transparency and trustworthiness in phishing detection systems. It begins with an overview of phishing attacks, their increasing sophistication, and the challenges faced by conventional detection techniques. A range of interpretable machine learning approaches, including rule-based models, decision trees, and additive models like Shapley additive explanations (SHAP), are surveyed. Their applicability in phishing detection is analyzed based on computational efficiency, prediction accuracy, and interpretability. The study also explores ways to integrate these methods into existing detection systems to enhance functionality and user experience. By providing insights into the decision-making processes of detection models, interpretable machine learning facilitates human supervision and intervention, strengthening overall system reliability. The paper concludes by outlining future research directions, such as improving the scalability, accuracy, and adaptability of interpretable models to detect emerging phishing techniques. Integrating these models with real-time threat intelligence and deep learning approaches could boost accuracy while preserving transparency. Additionally, user-centric explanations and human-in-the-loop systems may further enhance trust, usability, and resilience in phishing detection frameworks.
Enhancing face mask detection performance with comprehensive dataset and YOLOv8 Thua Huynh, Trong; Thanh Nguyen, Hoang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2634-2645

Abstract

In the context of the COVID-19 pandemic and the risk of similar infectious diseases, monitoring and promoting public health measures like wearing face masks have become crucial in controlling virus transmission. Deep learning-based mask recognition systems play an important role, but their effectiveness depends on the quality and diversity of training datasets. This study proposes the diverse and robust dataset for face mask detection (DRFMD), designed to address limitations of existing datasets and enhance mask recognition models' performance. DRFMD integrates data from sources such as AIZOO, face mask detector by Karan-Malik (KFMD), masked faces (MAFA), MOXA3K, properly wearing masked face detection dataset (PWMFD), and the Zalo AI challenge 2022, comprising 14,727 images with 29,846 instances, divided into training, validation, and testing sets. The dataset's scale and diversity ensure higher accuracy and better generalization for mask recognition models. Experiments with variations of the YOLOv8 model (n, s, m, l, x), an advanced object detection algorithm, on the DRFMD dataset, demonstrate superior performance through metrics like precision, recall, and mAP@50. Additionally, comparisons with previous dataset like FMMD show that models trained on DRFMD maintain strong generalization capabilities and higher performance. This study significantly contributes to improving accuracy of public health monitoring systems, aiding in the prevention of hazards from infectious diseases and air pollution.
Hybrid convolutional vision transformer for extrusion-based 3D food-printing defect classification Mawardi, Cholid; Buono, Agus; Priandana, Karlisa; Herianto, Herianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3311-3323

Abstract

Deep learning is generally used to perform remote monitoring of three-dimensional (3D) printing results, including extrusion-based 3D food printing. One of the widely used deep learning algorithms for defect detection in 3D printing is the convolutional neural network (CNN). However, the process requires high computational costs and a large dataset. This research proposes the Con4ViT model, a hybrid model that combines the strengths of vision transformer with the inherent feature extraction capabilities of CNN. The locally extracted features in the CNN were merged using the transformers’ global features with four transformer encoder blocks. The proposed model has a smaller number of parameters compared to other lightweight pre-trained deep learning models such as VGG16, VGG19, EfficientNetB2, InceptionV3, and ResNet50. Thus, the proposed model is simplified. Simulations were conducted to classify defect and non-defect images obtained from the printing results of a developed extrusion-based 3D food printing device. Simulation results showed that the model produced an accuracy of 95.43%, higher than the state-of-the-art techniques, i.e., VGG16, VGG19, MobileNetV2, EfficientNetB2, InceptionV3, and ResNet50, with accuracies of 77.88, 86.30, 82.95, 90.87, 84.62, and 93.83%, respectively. This research shows that the proposed Con4ViT model can be used for 3D food printing defect detection with high accuracy.

Page 148 of 173 | Total Record : 1722

 146   147   148   149   150 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue