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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 1,808 Documents
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An artificial intelligence technology for promoting hom-thong banana agriculture system Tanveenukool, Ratsames; Somsuphaprungyos, Suwit; Nokkurth, Boonyarit; Chamuthai, Likit; Bonguleaum, Patumwadee; Natho, Parinya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp568-579

Abstract

The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.
Instagram influencer classification using fine-tuned BERT model Sutramiani, Ni Putu; Dwikasari, Ni Made Dita; Trisna, I Nyoman Prayana; Darma, I Wayan Agus Surya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp1009-1018

Abstract

Influencer marketing has emerged as a powerful strategy in today’s digital world, where social media stars can influence how people think about products. However, the rapid growth of influencers and social media users presents novel challenges for brands in identifying suitable influencers for their marketing goals. Traditional approaches that rely on popularity and follower count are no longer the primary metrics for determining an influencer’s ability to affect consumer behavior. To address this gap, this study proposed an influencer classification to enhance audience targeting and marketing effectiveness. By utilizing deep learning, specifically fine tuned bidirectional encoder representations from transformers (BERT), influencer classification was carried out for Instagram users in Indonesia based on their post captions. The multilingual BERT model is optimized through hyperparameter tuning, including learning rate, batch size, and stop word removal variation. With an outstanding 80% accuracy, the model performs best in situations where stop words are not removed. This study on influencer classification using a fine-tuned BERT model has demonstrated the effectiveness of BERT in enhancing influencer selection. It contributes to the digital marketing domain by showcasing the potential of deep learning for social media analysis and content classification, paving the way for future data-driven marketing strategies.
Predicting trapped victims in debris using signal analysis ensemble classification Adama Jiya, Enoch; B. Oluwafemi, Ilesanmi; O. Ogundile, Olayinka; P. Babalola, Oluwaseyi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp493-505

Abstract

One major difficulty in pervasive computing is trapped human detection in search and rescue (SAR) scenarios. Accurately identifying trapped individuals is challenging due to noisy data and the curse of dimensionality. When non-line-of-sight (NLOS) conditions are present during catastrophic occurrences, the curse of dimensionality can result in blind spots in detections because of noise and uncorrelated data. Because machine learning algorithms are incredibly accurate, this work focuses on using ultra wideband (UWB) radar waves to detect individuals in NLOS scenarios and leveraging wireless communication to harmonize information. The paper uses ensemble methods to extract features using independent component analysis (ICA) and evaluate classification performance on both static and dynamic datasets. The testing results confirm the effectiveness of the proposed strategy, with classification accuracies of 87.20% for dynamic data and 88.00% for static data. Lastly, during SAR operations, our approach can assist engineers and scientists in making quick decisions.
An efficient method to improve machine learning decoders using automorphisms group Idrissi, Imrane Chemseddine; Nouh, Said; Bellfkih, El Mehdi; El Assad, Mohammed; Marzak, Abdelaziz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp547-558

Abstract

The decoding of error-correcting codes (ECCs) is a critical aspect of communication systems, yet traditional decoding techniques can often be computationally demanding or ineffective for certain codes, necessitating innovative approaches. In this study, we introduce a hybrid approach that combines machine learning and automorphism techniques to optimize the decoding process. Specifically, we train multilayer perceptron (MLP) models to learn the mapping between error syndromes and their corresponding errors. While these models exhibit robust learning capabilities, their performance sometimes does not reach 100%. To mitigate this limitation, we exploit the automorphism group of the code—a set of structure-preserving transformations—to convert the errors that the MLP struggles to decode into ones it can process more effectively. We use a minimum number of p permutations, pre-calculating and storing all possible automorphisms to ensure computational efficiency. Our experimental results reveal that this hybrid approach substantially enhances the decoding performance of the MLP model, presenting a promising avenue for decoding ECCs. Importantly, this approach is not limited to MLP models and can be applied to any machine learning model with a learning score less than 100%, broadening its applicability and impact. By integrating machine learning with traditional algebraic coding theory, we propose a new paradigm that holds the potential to revolutionize the design of decoding systems, making them more efficient and effective.
Deep intelligence for sustainable farming: a swarm-empowered data analytics architecture Muniswamy Panduranga, Kiran; Ranganathasharma, Roopashree Hejjaji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp901-908

Abstract

The inclusion of complex patterns of data in precision agriculture (PA) induces a greater degree of challenges from the perspective of carrying out conventional analytical operations. Although proliferated use of artificial intelligence (AI) has been noticed to yield some promising results to address such issues, yet they too have many shortcomings. Hence, the current manuscript introduces an innovative hybrid AI scheme towards enhancing the analytical operations necessary for decision-making in smart farming. The proposed scheme hybridizes a deep neural network (DNN) with a novel swarm intelligence (SI) model for optimizing the performance of its adopted deep learning (DL) model. Tested on a standard dataset of agriculture, the proposed model exhibited a 10% increase in accuracy and 40% faster response time when compared with conventional machine learning (ML) models, DL models, and SI models. The study contributes to a novel benchmark towards time-efficient, scalable, and intelligent analytics on PA.
Classifying mental workload of esports players using machine learning Fawwaz, Aisy Al; Rahma, Osmalina; Ittaqillah, Sayyidul Istighfar; Shane Kurniawan, Angeline; Putri, Revita Novianti; Varyan, Richa; Adinda, Aura; Ain, Khusnul; Chai, Rifai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp469-480

Abstract

Electrodermal activity (EDA) peak counts, derived from both tonic and phasic components, are widely used as physiological proxies for mental workload in cognitively demanding tasks, such as esports. However, their specificity remains uncertain, particularly given potential confounding effect of time-on-task. This study analyzes 92 competitive gameplay sessions from a multimodal esports dataset using three decomposition techniques: convex decomposition (cvxEDA), sparse deconvolution (sparseEDA), and time varying sympathetic activity (TVSymp). From each method, phasic, and tonic peak counts (TPC), as well as their normalized rates, were extracted. We examined their relationship with self-reported workload through correlation analyses, partial correlations controlling for session duration, and linear mixed-effects models (LMMs). While both peak types exhibited strong positive correlations with gameplay duration (r=0.915 for phasic and r=0.856 for tonic), their association with perceived workload vanished once time was accounted for. Across methods, TVSymp yielded the highest discriminative validity with an area under curve (AUC) of 0.880 in classifying high versus low workload. Machine learning (ML) classifiers trained solely on EDA-based features under a leave-one-subject-out (LOSO) scheme outperformed multimodal models that incorporated heart rate variability (HRV). These results underscore need to disentangle temporal structure from cognitive signals when interpreting EDA and call into question the assumption that EDA peak counts alone reliably encode mental workload across individuals.
Optimization of maximum power point tracking in wind energy systems: a comparative study of ant colony and genetic algorithms Mrabet, Najoua; Benzazah, Chirine; Chakib, Mohssine; Ziraoui, Adil; El Akkary, Ahmed; Laaroussi, Najma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp399-411

Abstract

This research focuses on optimizing maximum power point tracking (MPPT) in wind energy conversion systems (WECS) using ant colony optimization (ACO) and genetic algorithm (GA). The study evaluates these two metaheuristic techniques to optimize the parameters of a proportional integral-derivative (PID) controller in order to maximize power output in a permanent magnet synchronous generator (PMSG)-based system. Simulations conducted in MATLAB/Simulink show that both ACO and GA effectively enhance MPPT performance by improving power output, DC bus voltage regulation, and torque stability. The results demonstrate the potential of metaheuristic algorithms to optimize wind energy conversion efficiency and support sustainable energy development.
Explainable rice yield from Sentinel-1 and Sentinel-2 satellite data for food security Tribuana, Dhimas; Sattar, Usman; Mide, Baharuddin; Dayanti, Dayanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp615-627

Abstract

Reliable, explainable crop-yield estimates are essential for food-security planning in data-sparse regions. We present a transparent pipeline for district-level (regency) rice yield prediction in Indonesia that fuses Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 normalized difference vegetation index (NDVI), and weather/reanalysis features. The system standardizes inputs per province, fixes a 16-day temporal key, and uses a small, auditable ensemble of tree models (gradient boosting+light gradient-boosting machine (LightGBM)). Trained on ≤2023 data and evaluated on a 2024 temporal hold-out, a joint West Java ∪ South Sulawesi model achieves root mean square error (RMSE)≈0.80 t/ha, mean absolute error (MAE)≈0.48 t/ha, and R-squared (R²)≈0.33 at regency scale. Feature importances and Shapley additive explanations (SHAP) confirm that phenology (NDVI peak, integral, green-up/senescence), SAR backscatter (vertical transmit-vertical receive/vertical transmit-horizontal receive (VV/VH)), and wind/pressure are consistent drivers under monsoon conditions. The workflow supports routine, one-click provincial updates and produces parity maps and error bars for actionable diagnostics. These results demonstrate that combining Sentinel-1, Sentinel-2, and basic meteorology delivers accurate, interpretable, and operational yield signals suited to Indonesia’s food security needs, while providing a clear recipe for scaling to additional provinces.
Securing cloud data with machine learning: trends, gaps, and performance metrics Ifeoluwa Omogbehin, Blessing; Sigwele, Tshiamo; Semong, Thabo; Maenge, Aone; Nedev, Zhivko; Hlomani, Hlomani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp44-55

Abstract

The increasing reliance on cloud computing has raised significant concerns about the security of data access control, as traditional models are insufficient in managing the dynamic and large-scale nature of cloud environments. This review evaluates machine learning (ML)-based approaches to improve cloud data security, with a particular focus on advancements in anomaly detection and insider threat prevention. Deep learning (DL) models emerge as the most dominant, utilized by 47% of the studies due to their superior ability to process large datasets and adapt to real-time environments. Random forest models are also prominent, being adopted in 20% of the studies for their strong performance in anomaly detection and categorization. TensorFlow stands out as the most widely used tool, featuring in nearly 37% of the reviewed works, while datasets like Amazon Access and computer emergency response team (CERT) are employed in 20% and 13% of the research, respectively. Anomaly detection and prevention are critical priorities, accounting for 41.2% of the research objectives. However, gaps remain, with 21.7% of the studies noting adversarial vulnerabilities and 13% identifying limitations in dataset diversity. The review recommends further development of ML models to address these challenges, expanding dataset diversity, and improving real-time monitoring techniques to enhance cloud data security.
Enhancing vehicular ad hoc network security through a trust based vehicular model for attack mitigation Shilpa, Shilpa; Prasanth, Thiruvenkadam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp247-256

Abstract

In vehicular ad-hoc networks (VANETs), ensuring secure and reliable communication is essential due to the growing threat of cyber-attacks. As attacks can disrupt data transmission and compromise user privacy and network integrity, it is vital to develop robust security solutions. Hence, this work introduces a trust-based vehicular security (TVS) model, which leverages trust metrics to enhance VANET security. The main objective was to establish secure connections between vehicles and infrastructure nodes, effectively mitigating attacks while maintaining higher throughput. The methodology integrated a dynamic trust evaluation model to prevent malicious activities and ensure secure data transmission. The TVS model’s performance was compared to an existing VANET model, showing improved results in terms of detection rate, misclassification rate, and throughput. The findings demonstrate an average misclassification rate of 22.75%, a detection rate of 14.77%, and a throughput of 11.45%, highlighting the superior effectiveness of the TVS model in attack-prone environments when compared with existing VANET models. The TVS model provides a promising security solution for VANETs, offering enhanced protection against denial-of-service (DoS) attacks and spoofing (cyber-attacks) with better accuracy and network performance. The novelty lies in the dynamic, multi-trust-based approach for secure communication in vehicular networks.

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