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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 1,974 Documents
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Multi-agent autonomous GeoAI framework for scalable and self-improving geospatial intelligence Kim-Son Nguyen; The-Vinh Nguyen; Van-Viet Nguyen; Thi-Minh-Hue Luong; Huu-Khanh Nguyen; Duc-Binh Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2201-2215

Abstract

Large language models (LLMs) have recently expanded the scope of automation across many application domains. In geographic information systems (GIS), however, many tasks still require specialized expertise and remain difficult for non-expert users. Recent studies have explored LLM-based geospatial analysis under a single-agent paradigm, but these early systems remain limited by weak coordination, limited error recovery, and dependence on proprietary artifacts. This study proposes multi-agent autonomous geospatial artificial intelligence (MA-GeoAI), a multi-agent architecture in which the planner, coder, validator, debugger, and knowledge agents collaborate through the LangGraph framework. The framework was evaluated on three case studies: population exposure assessment, mobility pattern analysis, and county-level mortality modeling. Unlike general-purpose multi-agent LLM frameworks, MA-GeoAI embeds spatial semantics, coordinate reference system (CRS) consistency checks, geometry validation, and operation-aware coordination directly into the control loop. Across repeated runs, all evaluated systems completed the controlled artifact contract; therefore, the analysis focuses on auditability, runtime, fallback behavior, and reproducibility rather than binary task-completion superiority.
Performance-optimized boosted hybrid ensemble model for diabetes risk prediction Prajakta Bhosale-Dhamdhere; Ganesh Pathak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2062-2080

Abstract

The proposed boosted hybrid ensemble (BHE) machine learning (ML) model utilizes the classification power which reduces the overfitting by bagging and generates better results using random forest (RF) and extreme gradient boosting (XGBoost). The paper presents the importance and impact of secondary features in type 2 diabetes prediction utilizing real-time self reported and hospital data. The research study shows that age, gender, body mass index (BMI), and glucose are the key prime factors and are also influence by the other factors like demographic conditions, eating, and activity styles to some extents. The paper presents transfer learning (TL) on the basis on standard Pima Indians diabetes dataset (PIMA) to apply hybrid 2-layer BHE model to predict and classify the records into diabetic and non diabetic class providing explanations to factors contributing to it. The result section shows the highest 98% accuracy for BHE with optimized model presenting recommendations as per careful considerations of World Health Organization (WHO) and American Diabetes Association (ADA) standards. The paper throws light on the need of life-style factors considerations and correction to establish causation and refine preventive strategies in avoiding or postponing type-2 occurrences in youth people. This paper present perfect integration of multifactorial data with high reliability of artificial intelligence (AI)-driven healthcare explainable models to generate recommendations utilizing TLs.
A scalable low-cost internet of things-based electronic nose for identifying chemical ripening in fruits Amit R. Welekar; Gaurav Vishnu Londhe; Amit Pimpalkar; Abhrendu Bhattacharya; Nilesh Shelke; R. Jeberson Retna Raj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2113-2124

Abstract

Indiscriminate use of chemical agents like calcium carbide and ethephon for the ripening of fruits poses grave health hazards, emitting carcinogenic and neurotoxic compounds. Here we present a new, scalable, inexpensive, internet of things (IoT)-enabled electronic nose (e-nose) AI-Bot system for the detection of chemically ripened fruits. This would involve the development of a system that uses an MQ-3 gas sensor to quantify the ethanol content, as well as an MQ-135 gas sensor with an ESP32 microcontroller to quantify even further the amount of volatile organic compounds (VOCs) suggestive of artificial ripening. Flutter-based mobile application allows real-time monitoring, ripening classification using machine learning (ML) algorithms, and logging the historical data. A small sample was taken for inter-document feature literature mining, modelling sensor behaviors according to voltage dividers and gas concentration resistance laws for robust calibration and classification performance. Validation studies were performed on mango, banana, and papaya fruit in the laboratory environment. Total 75 samples (25 each of banana, mango, papaya across 3 trials) of fruit were tested. The implemented system achieved 95% for banana, 92% for mango, and 90% classification accuracy for papaya when cross validated.
Computed tomography imaging radiomics: a novel approach to early-stage non-small cell lung cancer prediction Raviteja Balekai; Mallikarjun S. Holi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2471-2483

Abstract

Radiomics shows promise as non-invasive method for enhancing clinical staging of non-small cell lung cancer (NSCLC) by using quantitative information from computed tomography (CT) scans. This study presents radiomics-based machine learning (ML) approach for staging NSCLC patients into clinical stages I, II, and III based on shape, intensity, and texture features. CT images of 369 NSCLC patients are collected from the cancer imaging archive (TCIA), and extracted 107 radiomic features following image biomarker standardization initiative (IBSI) protocol. The analysis of the sources of variability due to different imaging protocols, using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), showed that these effects were resolved through ComBat harmonization. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) are used for feature selection. Five ML algorithms: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) were used, with an 80:20 train-test split and 10-fold cross-validation. The classifier is assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. The RFE and RF classifier combination performed the best with AUROC of 0.9307 and accuracy of 0.8114. This study illustrates the use of radiomics models in non-invasive classification of NSCLC stages and it is role in clinical decision making.
Forecasting world sugar contract futures using long short-term memory technique with multi-step ahead forecasting strategy Khairil Anwar Notodiputro; Kayla Fakhriyya Jasmine; Indahwati Indahwati; Wandee Wanishsakpong
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2633-2642

Abstract

Time series analysis using stochastic and dynamic models for data forecasting is a key in assisting planning and decision-making processes in various sectors. Long short-term memory (LSTM), with its advantage in understanding patterns and non-linearity in sequential data, is applied in a multi-step ahead forecasting strategy on world sugar futures prices. Fluctuations in sugar prices have a significant impact on the agriculture, trade, and food industry sectors. Forecasting sugar prices becomes a crucial tool for industries, investors, and traders to anticipate changes and make informed decisions. The objectives of this study are to identify the best strategy for forecasting the world sugar contract price and to perform forecasting using the best model. The research results indicate that hyperparameter tuning in LSTM models produces varied combinations and effects. Furthermore, the recursive strategy is suitable for long-term forecasting, while the direct strategy is appropriate for short-term forecasting. Forecasting values for long-term periods remains challenging in achieving high accuracy.
Modified gorilla troops optimization for the quadratic assignment problem Hussein Fouad Almazini; Salah Mortada; Hassan Al-Mazini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2153-2165

Abstract

Balancing exploration and exploitation remain a fundamental challenge in artificial intelligence-based optimization, particularly when addressing discrete combinatorial problems such as the quadratic assignment problem (QAP). The gorilla troops optimizer (GTO), inspired by the collective social behavior of gorillas, has shown promising results in continuous domains but faces limitations when directly applied to discrete optimization. To address this, the present study introduces a modified gorilla troops optimizer (MGTO), a novel discrete adaptation designed specifically for the QAP. The proposed MGTO strategically integrates a swapping-based diversification mechanism to enhance exploration within discrete solution spaces, while a modified uniform crossover operator promotes effective exploitation of high-quality solutions. Extensive experiments on benchmark instances from the quadratic assignment problem library (QAPLIB) show that MGTO achieves superior convergence behavior and solution quality compared with several state-of-the-art algorithms. These results demonstrate MGTO’s capacity to maintain a balanced equilibrium between exploration and exploitation, effectively navigating complex discrete landscapes to yield high-quality solutions with strong computational efficiency.
Improving the quality of images using Wasserstein generative adversarial networks for image restoration Aruna Pavate; Surekha Janrao; Rohini Patil; Maganti Venkatesh; Shudhodhan Bokefode; Yunfei Li; Ubaldo Comite
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2907-2919

Abstract

In the present digital age, it is crucial to preserve personal memories and historical photographs in their original form, and this is made possible through image restoration. This paper presents a dynamic multi-scale Wasserstein generative adversarial network with gradient penalty (WGAN GP) framework that combines colorization and image denoising, addressing the limitations of distinct restoration models that denoise and colorize images in parallel. The proposed system adapts to hierarchical image features, stabilizes training, and enhances fine-grained texture reconstruction. The model is trained on CelebA, Places365, and ImageNet datasets. The need for repeated retraining is required, and there are still no guarantees of robustness under various degradations such as fading, saturation loss, and sensor noise. The results show peak signal-to-noise ratio (PSNR) of 24.5 dB and structural similarity index measure (SSIM) of 0.74, outperforming Pix2Pix, CycleGAN, denoising generative adversarial network (D‑GAN), and enhanced super‑resolution generative adversarial network (ESRGAN) in efficiency and robustness. In contrast to previous GAN-based restoration methods that treat denoising and colorization as separate problems, the presented multi-scale WGAN-GP applied a generator-discriminator model, resulting in less training redundancy and similar SSIM results while using ~55-65% less number of training epochs than ESRGAN and DeblurGAN. In the future, the model will integrate attention and transformer-based refinement to enhance detail recovery and perceptual realism further.
VisionEyeNet: a customized deep learning framework for early diagnosis of keratitis and uveitis Somashekhar Bannur Mayigowda; Raghavendra Kodandarama; Sudhamani Mallaiah; Manjunath Naganna; Jamuna Jamuna; Kiran Kumar B. S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2709-2722

Abstract

Keratitis and uveitis are increasingly prevalent ocular disorders, often linked to delayed detection and limited specialist access, particularly in rural healthcare settings. These diseases can lead to severe visual impairment or irreversible blindness if not identified at an early stage. Traditional diagnostic approaches are manual, time-consuming, and prone to human error, making them challenging for large-scale screening. To address these limitations, this study presents VisionEyeNet, a framework for automatic classification of keratitis and uveitis. VisionEyeNet integrates MobileNetV2 and DenseNet121 within a fusion architecture, along with image enhancement methods such as adaptive gamma correction and specular reflection suppression. The model was trained and evaluated on a curated dataset of 1,860 slit-lamp images (960 uveitis and 900 keratitis) using a patient-wise split (71.5% training, 8.4% validation, and 20% testing). On the independent test set, it achieved 98.0% accuracy (95% CI: 97.1–98.8%) with balanced performance across classes. Inference analysis showed an average processing time of 51±2 ms per image, supporting real-time use. These results indicate that VisionEyeNet has strong potential as a clinically useful decision-support tool, particularly in resource-limited settings.
Predictive modeling for crop suitability and productivity using machine learning techniques Gulaganjihalli Ningegowda Shwetha; Bhat Geetalaxmi Jairam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2533-2542

Abstract

With the increasing global population and rising food demand, improving agricultural productivity through data-driven decision support systems has become essential. This study proposes a cross-validated meta-stacking ensemble framework for multi-class crop suitability prediction using soil nutrient and environmental parameters. The dataset consists of 2,200 samples covering 22 crop types and seven predictor variables, including nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall. Six machine learning (ML) models—random forest (RF), decision tree (DT), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), support vector classifier (SVC), and k-nearest neighbors (KNN)—were trained and optimized using RandomizedSearchCV with k-fold cross-validation. A stacked ensemble model was then developed to combine heterogeneous learners and improve predictive robustness. Experimental results demonstrate that the RF model achieved an accuracy of 99.36%, while the proposed cross-validated meta-stacking ensemble achieved comparable performance with improved generalization stability. Precision, recall, and F1-score values of 0.99 indicate consistent classification across all crop classes. Feature importance analysis revealed N, K, and rainfall as the most influential predictors. Model robustness was evaluated using cross-validation and an independent test split to minimize overfitting risk. The findings highlight the effectiveness of ensemble learning for sustainable recommendation systems.
Diagnosing tuberculosis from X-ray imaging based on contrast limited adaptive histogram equalization Nguyen Trong Vinh; Lam Thanh Hien; Ha Manh Toan; Do Nang Toan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2567-2580

Abstract

Tuberculosis is a serious threat, and one of the effective data types for diagnosing tuberculosis is chest X-ray data. In this paper, we hypothesize the effect of image enhancement on the effectiveness of deep learning models in the problem of diagnosing pulmonary tuberculosis from chest X-ray images. To clarify the hypothesis, we have designed a data processing process with an image enhancement step using the contrast limited adaptive histogram equalization (CLAHE) technique to enhance the quality of input chest X-ray data, and the experiments were conducted with a standard dataset that was published on the Kaggle system. The evaluation is performed comprehensively with popular convolutional neural network architectures, including DenseNet201, DenseNet121, EfficientNetB0, and MobileNetV2, compared in two scenarios with and without the image enhancement step. Experiments have shown that the image enhancement step effectively improves the classification performance of all models, clearly through important scores such as area under curve (AUC), accuracy, F1-score, precision, and recall. The best result tested is the EfficientNetB0 model with 0.925926 accuracy score, 0.970732 AUC score, 0.904762 precision score, 0.95 recall score, and 0.926829 F1-score. In addition, qualitative analysis using gradient-weighted class activation mapping (Grad-CAM) shows that the resulting models have shown a focus on the lung region, reflecting the interpretability and suitability for radiologist expertise.

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