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
 131   132   133   134   135 
Classification of upper gastrointestinal tract diseases using endoscopic images Tran, Thanh Hai; Nguyen, Van-Tuan; Dao, Viet-Hang; Nguyen, Phuc-Binh; Nguyen, Thanh-Tung; Vu, Hai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp833-842

Abstract

Automatic classification and disease detection in medical images, aided by machine learning, provide crucial support to prevent overlooked instances and ensure prompt treatment of diseases. Despite impressive achievements in the field of polyp detection from endoscopic images, classification of other diseases, such as reflux esophagitis, esophageal cancer, gastritis, gastric cancer, and duodenal ulcer, is still subject to significant limitations and remains a challenging area of study because of their different and more challenging characteristics. This paper proposes a method to roughly classify the diseases from the whole images by deep learning. In particular, we focus on identifying hard samples from the training dataset and enriching them with some fundamental augmentation techniques. We then employ a cutting-edge model, specifically ResNet, for the final classification stage. Additionally, we enhance the original ResNet’s loss function by incorporating another loss function called focal loss. These modifications play a crucial role in boosting the accuracy of the ResNet model. Our proposed method outputs the disease category and corresponding heat map showing the area of interest. It achieved very promising accuracy (99.55%) for the classification of five lesions on our self-collected dataset. It serves a dual purpose. Firstly, it aids in the training of novice endoscopists, enabling them to gain valuable experience. Secondly, it offers a rapid solution for annotating extensive volumes of endoscopic image data at the label level.
Novel similarity measures for Fermatean fuzzy sets and its applications in image processing Romisa, Romisa; Vashist, Shruti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1049-1055

Abstract

Digital imaging is growing in our day-to-day life ranging from selfies to medical imaging. The extended applications of the field open doors for the researchers in the present-day context. The extraction of useful information from digital images is crucial because it depends on the various characteristics of the image. Fuzzy theory provides a better understanding of the image characteristics and, thus extracts meaningful information, even under uncertain situations. The present study reports the Fermatean fuzzy sets (FFSs) application in image processing while proposing similarity measures. These similarity measures highlight the perfect and precise results from an image while using multiple parameters of the image for information extraction. The study concludes that the proposed similarity measures provide a better estimation of data from an image used in image processing problems.
Crop classification using object-oriented method and Google Earth Engine Desai, Geeta T.; Gaikwad, Abhay N.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1271-1280

Abstract

Agriculture crop monitoring in real-time is crucial in formulating effective agricultural practices and management policies. The primary goal of the investigation is to explore how the utilization of Sentinel-1 data and its fusion with Sentinel-2 impact crop classification accuracy in a fragmented agricultural landscape in the Yavatmal District of Maharashtra, India. Pixel based classification and object-oriented classification approaches were implemented on Google Earth Engine (GEE), and obtained results were compared for different combinations of optical and microwave features. The research revealed that the object-based technique performed better than the pixel-based approach, with a 3.5% increase in overall accuracy. For 2022, crop-type mapping was generated with overall accuracies varying from 85.5% to 61% and a kappa coefficient between 0.77 and 0.37. These overall accuracies imply that joint use of optical and radar data has given a 24% improvement in overall accuracy compared to use of only optical data. In addition, the temporal change in the backscatter coefficients and different vegetation indices for different crops were examined over crop growth cycle. This work demonstrates the fusion of Sentinel-1 and Sentinel-2 data to classify wheat, chickpea, other crops, water and urban areas.
Enhanced intrusion detection through dual reduction and robust mean Taha, Archi; Benattou, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1260-1270

Abstract

The exponential growth of online networks necessitates a paradigm shift in intrusion detection systems (IDS). Traditionalmethods falter under the massive influx of data, resulting in high false positives and reduced detection accuracy. This research introduces a novel approach combining principal component analysis (PCA) and linear discriminant analysis (LDA), augmented by robust generalized sample mean, to enhance IDS performance. PCA efficiently reduces data dimensionality, while LDA extracts critical features that differentiate normal network traffic from anomalies. The robust generalized sample mean counteracts the influence of outliers, ensuring accurate and reliable analysis. Implemented on the UNSW-NB15 dataset, our method achieves an average 6% reduction in false positives and a 10% increase in detection rate. Additionally, our testing methodology closely mirrors real-world conditions, making the results more representative of practical scenarios compared to existing work. These advancements demonstrate substantial improvements in IDS performance and robustness over existing techniques.
A mixed integer nonlinear programming model for site-specific management zone problem Urban-Rivero, Luis Eduardo; Velasco, Jonás
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1096-1105

Abstract

Precision agriculture employs sophisticated tools to optimize decision-making in farming, aiming to simultaneously improve crop yields and manage resources more effectively in a context of increasing scarcity and rising costs. A key aspect of precision agriculture is the delineation of site-specific management zones (SSMZs), which involves segmenting a field into areas that are homogeneous in terms of soil physicochemical properties. The problem of delineating SSMZ have been approached using a wide variety of methodologies, all of which, heuristic, focus on finding feasible solutions. Until this work, there was no exact algorithm or mathematical model that would allow for a point of comparison. This paper introduces a novel approach to tackle the delineation of SSMZ with orthogonal shapes through the development of a mixed integer nonlinear programming (MINLP) model. Small instances with different scenarios show the scope of the proposed approach and the significance of the results. It provides a structure for the SSMZ problem with orthogonal shapes and establishes a benchmark for evaluating the performance of heuristic solutions, metaheuristics, or hybrid approaches.
A comparative analysis of exponential smoothing method and deep learning models for bitcoin price prediction Tripathy, Nrusingha; Mishra, Debahuti; Hota, Sarbeswara; Priyadarshani Behera, Mandakini; Chandra Das, Gobinda; Sekhar Dalai, Sasanka; Nayak, Subrat Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1401-1409

Abstract

Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.
Quality and shelf-life prediction of cauliflower using machine learning under vacuum and modified atmosphere packaging Hosen, Md. Apu; Md. Galib, Dr. Syed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp907-916

Abstract

Ensuring the freshness and quality of cauliflower during storage and transportation is essential due to its high perishability. This study harnesses the power of machine learning to predict the quality and shelf-life of cauliflower under cost-effective vacuum and modified atmosphere packaging (MAP) techniques. By investigating key parameters such as total soluble solids (TSS), pH, weight loss, and color change, a significant impact on post-packaging quality was identified. To address the challenge of accurate color change measurement, an innovative method utilizing a bilateral filter for noise reduction and particle swarm optimization (PSO) with Markov random field (MRF) segmentation was developed. TSS, weight loss, and color change were identified as key parameters, and leveraging these parameters, artificial neural networks (ANN) were employed to create highly precise predictive models, achieving R-squared values of 0.952 for TSS, 0.992 for weight loss, and 0.981 for color change. This approach not only enhances the efficiency and sustainability of food production and distribution but also minimizes food waste and maximizes profitability for cauliflower in global markets through the use of cost-effective packaging solutions.
Heart disease approach using modified random forest and particle swarm optimization Barry, Khalidou Abdoulaye; Manzali, Youness; Flouchi, Rachid; Elfar, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1242-1251

Abstract

For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random forest (MRF), a new approach (PSO-MRF) is proposed to predict heart disease. The main purpose is to select the important features after the bootstrap method for each decision tree in the random forest, and then optimize the MRF by the PSO algorithm. The experiments are carried out using the publicly accessible UCI heart disease datasets. Thorough experimental analysis demonstrates that our approach has outperformed the random forest algorithm as well as many other classifiers. This model helps doctors and researchers improve the diagnosis and treatment of heart disease, resulting in more prompt, accurate patient care.
Video forgery: An extensive analysis of inter-and intra-frame manipulation alongside state-of-the-art comparisons Shaikh, Sumaiya; Kannaiah, Sathish Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1471-1483

Abstract

The widespread accessibility of inexpensive mobile phones, digital cameras, camcorders, and security closed-circuit television (CCTV) cameras has resulted in the integration of filmmaking into our everyday existence. YouTube, Facebook, Instagram, and Snapchat are a few of the video-sharing and editing applications that facilitate the process of uploading and editing videos. Additional instances include Adobe Photoshop, Windows Movie Maker, and Video Editor. Although editing has its advantages, there is a potential risk of counterfeiting. This occurs when films are edited with the intention of misleading viewers or manipulating their perspectives, which can be particularly troublesome in judicial procedures where recordings are submitted as evidence. The issue has been exacerbated by the emergence of deep learning methods, such as deepfake videos that effectively manipulate facial characteristics. Consequently, individuals have become less reliant on visual evidence. These issues emphasise the pressing necessity for the creation of dependable methods to determine the authenticity of films and identify cases of fraud. Contemporary methods can depend on assessing modified frames or utilising distortions generated during video codec compression or double compression. Since 2016, multiple studies have been undertaken to investigate techniques, strategies, and applications to tackle this problem. The objective of this survey study is to provide a comprehensive analysis of these algorithms, highlighting their advantages and disadvantages in detecting different forms of video forgeries.
Squeeze-excitation half U-Net and synthetic minority oversampling technique oversampling for papilledema image classification Wiharto, Wiharto; Syaifuddin, Angga Exca Pradipta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1410-1419

Abstract

The emergence of various convolutional neural networks (CNN) architectures indicates progress in the computer vision field. However, most of the architectures have large parameters, which tends to increase the computational cost of the training process. Additionaly, imbalanced data sources are often encountered, causing the model to overfit. The aim of this study is to evaluate a new method to classify retinal fundus images from imbalanced data into the corresponding classes by using fewer parameters than the previous method. To achieve this, squeeze-excitation half U-Net (SEHUNET) architecture, a modification of half U-Net with squeeze-excite process to provide attention mechanism on each feature maps channel of the model, in combination with synthetic minority oversampling technique (SMOTE) is proposed. The test accuracy of SEHUNET is 98.52% with area under the curve of receiver operation characteristic (AUROC) of 0.999. This result outperforms the previous study that used CNN with Bayesian optimization, achieving accuracy of 95.89% and AUROC of 0.992. SEHUNET is also able to compete with the transfer learning methods used in previous research such as InceptionV3 with 96.35% accuracy, visual geometry group (VGG) with 96.8%, and ResNet with 98.63%. This performance can be achieved by SEHUNET with only 0.268 million parameters compared to the architecture parameters used in previous research ranging from 11 million to 33 million.

Page 133 of 173 | Total Record : 1722

 131   132   133   134   135 

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