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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 86 Documents
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Search results for , issue "Vol 15, No 1: February 2026" : 86 Documents clear
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.
Evaluating the detected communities using traditional algorithms on keyword co-occurrence networks R., Kiruthika; Sakkarapani, Krishnaveni
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.pp919-928

Abstract

Community detection is one of the most significant research areas in network analysis, which helps to understand the internal structure of large networks. This work utilizes the traditional community detection methods on a keyword co-occurrence graph derived from the Scopus bibliographic database. This research article primarily focused on the index keywords of deep learning driven publications obtained from three major network Scopus bibliometric datasets (SBD), namely SBD_1 as 2006-2013, SBD_2 as 2014-2016, and SBD_3 as 2017. For this proposed model framework, the existing traditional algorithms, including Louvain, greedy modularity optimization (GMO), Leiden, Infomap, speaker-listener label propagation algorithm (SLPA), Walktrap, SpinGlass, K-Clique, and Clauset, Newman and Moore (CNM) methods are applied to detect communities from the network and carried out through Python. Comparisons among these algorithms, Leiden, SpinGlass, and Louvain are considered as better algorithms for our work based on the detected communities, modularity score and other metrics to evaluate the performance of detected communities from the network. This research proposes an ideology for the selection process of algorithms that depends on different factors like network characteristics, network structure, dataset size, and computational efficiency. This analysis suggests a unique perspective on the effectiveness of each method in the Scopus bibliometric network and its potential to enhance research topic exploration.
Interpretable artificial intelligence system for personalized cognitive stimulation Baena-Navarro, Rubén; Carriazo-Regino, Yulieth; Macea-Anaya, Mario
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.pp164-176

Abstract

The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.

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