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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
 163   164   165   166   167 
A merchant analytics framework for revenue forecasting and financial stress detection using transaction data Harb, Yara; Baaklini, Wissam; Abbas, Nadine; Kadry, Seifedine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4848-4864

Abstract

By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.
Securing post-quantum cryptography: side-channel resilience in CRYSTALS-Kyber key encapsulation mechanism Kasture, Shreyas; Maurya, Sudhanshu; Singh, Alakshendra Pratap; Shukla, Amit; Kotiyal, Arnav; Mirza, Kashish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5251-5267

Abstract

This study evaluates side-channel vulnerabilities in hardware implementations of the cryptographic suite for Algebraic lattices (CRYSTALS)-Kyber key encapsulation mechanism (KEM) using correlation and differential power analysis (DPA) techniques. Unprotected field-programmable gate array (FPGA) implementations across all Kyber parameter sets were successfully compromised, revealing significant information leakage. Attack complexity scaled linearly with key size. Additive Boolean masking provided varying protection levels, with 4-bit masking offering a 100× security increase at notable performance cost. Performance characterization showed increased slice utilization and reduced maximum frequency for higher-order masking. A novel hybrid countermeasure combining higher-order masking with controlled time randomization enhanced protection against machine learning-based attacks. Comprehensive power trace analysis using 12-bit precision at 500 MS/s sampling rates was conducted. Statistical evaluation utilized Pearson's correlation and Welch's t-tests with a 0.8 threshold for key recovery. Real world validation in IoT, financial, and satellite scenarios highlighted practical post-quantum cryptography (PQC) deployment challenges. The study provides concrete design guidance for efficiently securing hardware Kyber implementations against side-channel attacks.
Spam social media profile detection using hybrid positive unlabelled learning Patel, Nidhi A.; Nanavati, Nirali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4838-4847

Abstract

Online social networks (OSNs) are a communication medium of social interaction for people, where social activities, entertainment, business oriented activities, and information are exchanged. It creates an environment with worldwide connectivity where groups of individuals may discuss their interests and activities on social media platforms. Billions of people routinely interact with social content, opinion sharing, recommendations, networking, scouting, social campaigns, alerting on OSNs. The increase in popularity of OSNs creates new challenges and perspectives to the researchers of social networks, which is of interest in various fields. One of the most popular networking platforms for microblogging is X (formerly Twitter). Millions of spam accounts have inundated the X network, which could damage normal users' security and privacy. Hence, the research in this filed has become essential for enhancing real users' protection and identifying spam profiles. In this manuscript, we propose hybrid approach based on semi-supervised learning to detect the spam profiles. The proposed work is based on the positive and unlabeled (PU) learning algorithm, which learns from an unlabeled dataset and a small number of positive instances. Simulation results demonstrate that our approach outperformed existing PU learning approach by 17.39% and 17.51% improvement respectively in spam detection rate on X and Instagram datasets.
Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning Montoya-Estrada, Claudia Nohemy; Cardona-Morales, Oscar; López-Naranjo, Oscar; Hernandez-Jorge, Freddy Eliseo; Garcés-Gómez, Yeison Alberto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4722-4730

Abstract

Ensuring food security and meeting the economic needs of farmers and nations depend heavily on detecting and preventing crop yield losses. Early detection of tar spot caused by Phyllachora maydis is crucial to implementing efficient mitigation actions in the earliest stages of infestation. Currently, visual methods are used for detection, which require extensive training and experience from the operator. However, remote sensing techniques can be used to detect tar spot infestation through the selection of wavelengths present in the maize plant spectral signature. This research proposes using machine learning techniques and logistic regression to determine the first stage of tar spot infestation. The results show that the logistic regression model is the most suitable for detecting this first stage, and the K-Nearest Neighbors Classification and Random Forest Classification algorithms generate the best classification results. This approach can significantly reduce costs in terms of time, labor, and subjective analysis.
Exploring the influence of soft information from economic news on exchange rate and gold price movements Prastowo, Rahardito Dio; Budi, Indra; Ramadiah, Amanah; Santoso, Aris Budi; Putra, Prabu Kresna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5231-5239

Abstract

Information on business conditions is an important concern for market players and regulators. Hard information relates to easily validated characteristics such as production levels and employment conditions. In contrast, soft information such as consumer and public perceptions—is subjective and difficult to verify. Although previous studies on hard and soft information mainly focus on microeconomics and banking, current developments in big data and machine learning enable broader applications in financial market analysis. This study combined VADER sentiment analysis and support vector machine (SVM) classification (accuracy=85%) to analyze economic news, followed by Granger causality and multiple linear regression to examine causal effects and predictive relationships. The findings reveal that negative news sentiment and the Indonesian Rupiah (IDR) exchange rate influence each other, while positive sentiment has no causal impact on the exchange rate. Both negative and positive sentiments affect gold prices, whereas gold price movements do not influence sentiment. Regression analysis shows that negative sentiment has a stronger effect in decreasing the IDR exchange rate than positive sentiment, with the model explaining approximately 20% of the variance. Integrating sentiment and exchange rate data enhances the predictive model for gold price forecasting and highlights the asymmetric roles of positive and negative news in financial dynamics.
Transformer and text augmentation for tourism aspect-based sentiment analysis Situmeang, Samuel; Tambunan, Sarah Rosdiana; Jevania, Jevania; Simanjuntak, Mastawila Febryanti; Sinaga, Sandro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4614-4622

Abstract

The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Deep learning-based feature selection for lung adenocarcinoma classification and biomarker discovery Bouazza, Sara Haddou; Bouazza, Jihad Haddou
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4703-4710

Abstract

Lung adenocarcinoma, a leading cause of cancer-related mortality, underscores the need for reliable diagnostic tools. This study proposes a robust multi-stage feature selection and classification framework for biomarker discovery, using the cancer genome atlas lung adenocarcinoma (TCGA-LUAD) as the primary dataset and GSE19188 for independent validation. The framework combines differential expression analysis (Wilcoxon rank-sum test), joint mutual information maximization (JMIM), and sparse autoencoder-based refinement to identify a compact and predictive set of five genes. These genes are involved in key lung cancer pathways, including epidermal growth factor receptor (EGFR) signaling, cell cycle regulation, and immune response, and include biomarkers such as surfactant protein A2 (SFTPA2), napsin an aspartic peptidase (NAPSA), and T-box transcription factor 4 (TBX4). The hybrid deep learning classifier achieved high accuracy (98.4%) and area under the receiver operating characteristic curve (AUC-ROC) (0.996) on TCGA-LUAD, with strong generalization on GSE19188 (accuracy: 96.7%, AUC-ROC: 0.993%). Overall, the framework offers an interpretable and effective solution for LUAD classification and biomarker identification.
Enhancing credit card fraud detection with synthetic minority over-sampling technique-integrated extreme learning machine Ajlan, Iman Kadhim; Mahdi, Mohammed Ibrahim; Murad, Hayder; AL-Dhief, Fahad Taha; Safie, Nurhizam; Shakir, Yasir Hussein; Abbas, Ali Hashim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4749-4762

Abstract

Many works in cybersecurity detection suffer from low accuracy rates, particularly in real-world applications, where imbalanced datasets and evolving fraud strategies pose significant hurdles. This study introduces an optimized extreme learning machine (ELM) algorithm to address these challenges by dynamically adjusting hidden nodes ranging from 10 to 100 with an increment step of 10 and integrating two activation functions. The proposed method utilizes the synthetic minority over-sampling technique (SMOTE) to handle class imbalance effectively and incorporates a comprehensive evaluation using descriptive statistics, visualization, and significance testing. The proposed ELM-SMOTE method achieves the highest results including an accuracy of 99.710%, recall of 85.811%, specificity of 99.743%, and G-mean of 92.068%. These outcomes reflect the robustness and adaptability of the proposed ELM algorithm in detecting fraudulent transactions. This study emphasizes the importance of a holistic performance analysis, addressing gaps in existing methods and providing a scalable framework for real-world fraud detection applications.
The use of geographic information systems to measure the financial performance of micro enterprises Lau, Elfreda Aplonia; Endayani, Sri; Kulsum, Umi; Stefano, Andrew; Rokhim, Abdul; Purbawati, Purbawati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5333-5343

Abstract

This study examines the application of geographic information systems (GIS) to measure and visualize the financial performance of micro enterprises in remote areas of East Kalimantan, Indonesia. Micro enterprises are crucial to local economies but often face barriers such as limited capital access, inadequate infrastructure, and insufficient business training. Using a mixed-method approach, the research combined surveys of 200 micro business owners, secondary economic data, and GIS-based spatial analysis. The results indicate clear spatial disparities: enterprises located closer to financial institutions and training programs achieved 25–30% higher profitability and stronger operational resilience. GIS mapping effectively identified performance clusters and underserved zones, providing actionable insights for targeted policy interventions. Key factors influencing financial outcomes include access to capital, training opportunities, and infrastructure quality. This study demonstrates the value of GIS as a decision-support tool for policymakers in designing spatially informed financial assistance, infrastructure planning, and mobile training deployment. The findings contribute to socio-economic planning discourse and propose a replicable GIS-based framework for strengthening microenterprise resilience in underdeveloped regions.
Prediction of flood-affected areas based on geographic information system data using machine learning Faruq, Amrul; Syafaah, Lailis; Irfan, Muhammad; Abdullah, Shahrum Shah; Mohd Hussein, Shamsul Faisal; Yakub, Fitri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4675-4683

Abstract

Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models, including random forest (RF) and support vector machine (SVM), to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.

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