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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 1,722 Documents
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Hybrid method for optimizing emotion recognition models on electroencephalogram signals Wirawan, I Made Agus; Ernanda Aryanto, Kadek Yota; Sukajaya, I N.; Agustini, Ni Nyoman Mestri; Widhiyanti Metra Putri, Dewi Arum
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.pp2302-2314

Abstract

Two critical factors that need to be studied in emotion recognition are the differences in electroencephalogram (EEG) signal patterns caused by participant characteristics and EEG signals with spatial information. These factors significantly affect the resulting accuracy. The model proposed in this study can consider these factors. This model consists of the modified weighted mean filter method for the basic EEG signal smoothing process, the differential entropy method for the feature extraction process, the relative difference method for the baseline reduction, the 3D cube method for feature representation, and the continuous capsule network method for the classification process. Based on testing on three public datasets, this hybrid method can overcome factors affecting emotion recognition accuracy. This statement is based on the accuracy produced by this model, which outperformed the accuracy validated in previous studies.
Blockchain and machine learning driven agricultural transformation framework to enhance efficiency, transparency, and sustainability Shivashankar, Shivashankar; Prasad Karani, Krishna; Rajgopal, Manjunath; Totad, Sarala; Giddappa, Erappa; Swamy, Shivakumar
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.pp1976-1988

Abstract

The agricultural sector is undergoing a transformative journey empowered by technological innovations. In this context, this research work endeavors to revolutionize the agricultural supply chain (ASC) by developing a comprehensive online platform that connects sellers, farmers, and customers. Through meticulous planning, design, and implementation, the system aims to streamline the process of buying and selling agricultural products, thereby fostering efficiency, transparency and accessibility. The key features include user registration, product management, order tracking, and blockchain-machine learning (ML) based transaction security. The proposed research work's success hinges on thorough testing and validation, ensuring its reliability and usability. By leveraging technology to bridge gaps in the agricultural ecosystem, this proposed work seeks to empower stakeholders and contribute to the sustainable growth of the agricultural industry. In the current agricultural landscape in India, traceability has been a significant challenge. The industry lacks a comprehensive system that provides visibility into the source and quality of produce. Our proposed system aims to address the shortcomings of the existing agricultural ecosystem by introducing a comprehensive solution powered by blockchain technology and advanced data processing techniques.
Enhanced Arabic-language cyberbullying detection: deep embedding and transformer (BERT) approaches Jaber Aljohani, Ebtesam; S. Yafooz, Wael M.
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.pp2258-2269

Abstract

Recent technological advances in smartphones and communications, including the growth of such online platforms as massive social media networks such as X (formerly known as Twitter) endangers young people and their emotional well-being by exposing them to cyberbullying, taunting, and bullying content. Most proposed approaches for automatically detecting cyberbullying have been developed around the English language, and methods for detecting Arabic-language cyberbullying are scarce. Methods for detecting Arabic-language cyberbullying are especially scarce. This paper aims to enhance the effectiveness of methods for detecting cyberbullying in Arabic-language content. We assembled a dataset of 10,662 X posts, pre-processed the data, and used the kappa tool to verify and enhance the quality of our annotations. We conducted four experiments to test numerous deep learning models for automatically detecting Arabic-language cyberbullying. We first tested a long short-term memory (LSTM) model and a bidirectional long short-term memory (Bi-LSTM) model with several experimental word embeddings. We also tested the LSTM and Bi-LSTM models with a novel pre-trained bidirectional encoder from representations (BERT) and then tested them on a different experimental models BERT again. LSTM-BERT and Bi-LSTM-BERT demonstrated a 97% accuracy. Bi-LSTM with FastText embedding word performed even better, achieving 98% accuracy. As a result, the outcomes are generalized.
Customer segmentation using association rule mining on retail transaction data Kajornkasirat, Siriwan; Gunglin, Pattarawan; Puangsuwan, Kritsada; Kaewsuwan, Nawapon
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.pp1919-1929

Abstract

This research aimed to investigate a suitable algorithm for customer segmentation using as customer behavior indicators the recency, frequency, and monetary (RFM) values of the customers. The clustering algorithms K-means, fuzzy C-means, and self-organizing neural network (SONN) were compared for finding the most appropriate algorithm. The customer segmentation was analyzed using association rule mining with the frequent pattern algorithm (FP-Growth). Data on retail transactions during January 2021 - May 2023 were obtained from Tuenjai Company, Thailand, with a total of 202,469 records. The results from the three algorithms were compared by the silhouette coefficient (SC), Calinski-Harabasz (CH) index, Davies-Bouldin (DB) index, iteration count, and execution time. The results showed that the K-means algorithm was the most suitable algorithm for customer segmentation in this study. K-means clustering grouped the customers into three groups here labeled as “important value”, “general development”, and “lost”, based on the RFM values. There were 38 rules for the important value segment, and two rules each for the general development and the lost groups. These results could be useful to the business organization for improving the customer experiences, increasing sales, preparing or promoting products, and stock management efficiency.
A symptom-driven medical diagnosis support model based on machine learning techniques Laabidi, Adil; Aissaoui, Mohammed
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.pp2072-2082

Abstract

Medicine is a human science that is constantly evolving, and this evolution generates a large mass of data that needs to be exploited with the multitude of IT resources available to guarantee and maintain this scientific progress. Some diseases share most symptoms, whereas others could have a low probability of being identified in an early stage. Thus, when facing a such situation, an inexperienced doctor may have difficulty making the right diagnosis or may test different cases, which will be a big waste of time. In this paper, we are going to make this embarrassing situation less complex by giving practitioners every probable disease, and even the least probable ones according to the given symptoms. Indeed, this work will push the diagnosis deeper to reveal hidden symptoms and pathogenesis, to help practitioners make the right decisions. To develop such a solution, the data is organized by matching each disease with its known symptoms, then we used naive Bayes as a classification model, and different metrics to evaluate the performance of this experiment. This work proves that machine learning has become very effective in the medical sector, especially when we notice that the accuracy exceeds 90% in the detection of diseases.
Early detection and classification of bone marrow changes in lumbar vertebrae using machine learning techniques Hussein Shakir, Yasir; Sieh Kiong, Tiong; Phing Chen, Chai
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.pp2132-2145

Abstract

Bone marrow changes in lumbar vertebrae (BMCLVB) have emerged as a significant correlation of chronic low back pain (CLBP) severity, especially in patients with comorbid conditions like HIV, osteoporosis, and cancer. Identifying these correlations not only aids governments and health insurance providers but also facilitates early treatment for those at risk. However, challenges lurk due to the unavailability and quality of healthcare data. The collaboration between data science and artificial intelligence, particularly machine learning (ML), has propelled biomedical research forward. So far, accessing and processing hospital and clinical data remains a hurdle. In doing so it aims to provide an opportunity for early intervention and treatment. In addition, the goal of the current study was to overcome data shortcomings using advanced ML techniques to unlock complex magnetic resonance imaging (MRI) features. We believe that extending the dataset with that obtained from an Iraqi hospital will not only assist in diagnosing BMCLVB but also fill the gap between data science and healthcare. Above all, the upgrade is intended to empower biomedical research and increase the chances of successful patient treatment.
Averaged bars for cryptocurrency price forecasting across different horizons El Youssefi, Ahmed; Hessane, Abdelaaziz; Zeroual, Imad; Farhaoui, Yousef
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.pp1910-1918

Abstract

Technical analysis uses past price movements and patterns to predict future trends and help traders make informed decisions about their cryptocurrency portfolios. This study investigates the effectiveness of different forecasting algorithms and features in predicting the future log return of cryptocurrency close price across various horizons. Specifically, we compare the performance of AdaBoost, light gradient boosting machine (LightGBM), random forest (RF), and k-nearest neighbor (KNN) regressors using Kline open, high, low, close (OHLC) prices data and averaged bars (Heikin-Ashi) features. Our analysis covers ten of the most capitalized cryptocurrencies: Cardano, Avalanche, Binance Coin, Bitcoin, Dogecoin, Polkadot, Ethereum, Solana, Tron, and Ripple. We have observed nuanced patterns in predictive performance across different cryptocurrencies, forecasting horizons and features. Then we have found that AdaBoost and RF models consistently exhibit a competitive performance, with LightGBM showing promising results for specific cryptocurrencies. The impact of forecast horizons on forecasting performance underscores the need for tailored forecasting models. In summary, the use of Kline OHLC data as features outperforms averaged bars in forecasting the first and second horizons, while averaged bars outperform Kline OHLC data for mid- to relatively long-term horizons (starting from the third horizon). Our findings suggest that averaged bars merit more attention from researchers instead of relying solely on Kline OHLC data.
Empowering SDN with DDoS attack detection: leveraging hybrid machine learning based IDPS controller for robust security G., Florance; Anandhi, R. J.
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.pp2479-2489

Abstract

Software-defined network (SDN) is an innovative networking framework where a centralized controller manages networking administration and sorts out network traffic issues. It becomes difficult for the controller to identify the malicious user who is sending a large number of spoofed packets, such as in a distributed denial of service (DDoS) attack. To prevent DDoS attacks from damaging legitimate users, it is important to take steps to prevent them. The issue of preventing DDoS attacks in SDN remains unresolved despite many algorithms proposed. Methods presented in this paper employ bandwidth threshold estimation, which triggers the intrusion detection and prevention system (IDPS) controller if the threshold is exceeded. Whenever the threshold is exceeded due to network congestion, transferred packets are filtered at the server level by identifying the utilization of bandwidth in OpenDaylight (ODL) and POX. K-nearest neighbor (K-NN) and support vector machine (SVM) are used by the IDPS controller to detect and thwart DDoS attacks. Using Mininet, two SDN centralized controllers are simulated to improve performance significantly. Based on SVM in the ODL controller, this work has provided mitigation techniques for preventing DDoS attacks with an accuracy of 96.75% compared to previously published accuracy.
A novel light-weight convolutional neural network for rice leaf disease classification Jayaraman, Parthasarathi; Palaniyandi, Muthulakshmi
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.pp2547-2558

Abstract

Rice is one of the primary sources of staple meals. It may turn out to be a disaster as the production of agricultural products is declined due to diseases and therefore it is required to straighten up the situation by taking precautionary measures. Generally, deep learning (DL) architectures are employed for the identification of plant leaf diseases and it is observed that there is a trade-off between the accuracy and parameters. This study introduces a light-weight architecture called rice leaf disease classification convolutional neural network (RLDC-CNN). The objective of the proposed architecture is to improve the accuracy and reduce the loss by using a combination of convolutional layers, maxpooling layers, and fully connected layers. These layers use activation function for non-linearity, dropout for regularization and implements hyperparameter tuning with various optimizers that include Adam, RMSprop, stochastic gradient descent (SGD) and adaptive gradient (AdaGrad). Experiments are conducted on the dataset of 7,096 images with batch size of 32 under various learning rates. The behavior is analyzed by comparing the existing models and the count of parameters (in millions) equipped by RLDC-CNN, DenseNet121, VGG-16, and ResNet50 is 0.65, 8.49, 15.44, 26.49 with accuracy of 99.15%, 98.94%, 97.82%, 96.48% respectively.
Camera-based advanced driver assistance with integrated YOLOv4for real-time detection Jayan, Keerthi; Muruganantham, Balakrishnan
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.pp2236-2245

Abstract

Testing object detection in adverse weather conditions poses significant chal lenges. This paper presents a framework for a camera-based advanced driver assistance system (ADAS) using the YOLOv4 model, supported by an electronic control unit (ECU). The ADAS-based ECU identifies object classes from real-time video, with detection efficiency validated against the YOLOv4 model. Performance is analysed using three testing methods: projection, video injection, and real vehicle testing. Each method is evaluated for accuracy in object detection, synchronization rate, correlated outcomes, and computational complexity. Results show that the projection method achieves highest accuracy with minimal frame deviation (1-2 frames) and up to 90% correlated outcomes, at approximately 30% computational complexity. The video injection method shows moderate accuracy and complexity, with frame deviation of 3-4 frames and 75%correlated outcomes. The real vehicle testing method, though demand ing higher computational resources and showing a lower synchronization rate (> 5 frames deviation), provides critical insights under realistic weather condi tions despite higher misclassification rates. The study highlights the importance of choosing appropriate method based on testing conditions and objectives, bal ancing computational efficiency, synchronization accuracy, and robustness in various weather scenarios. This research significantly advances autonomous ve hicle technology, particularly in enhancing ADAS object detection capabilities in diverse environmental conditions.

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