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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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Autoencoder and GAN-aided plant disease detection in rice and cotton via hybrid feature extraction and decision tree classification Naduvinamani, Anandraddi; Rudagi, Jayashri; Anandhalli, Mallikarjun
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.pp707-724

Abstract

In agriculture, crop diseases caused by pathogens, including bacteria, viruses, and fungi, pose a significant threat to the effectiveness of agricultural productivity. Some major crops in India such as rice and cotton are adversely impacted, leading to economic loss and loss of production. Timely intervention and sustainable agriculture depend on proper and early identification of diseases. In this paper, we propose a novel plant disease detection framework that integrates generative adversarial network (GAN) based image denoising with feature extraction and decision tree (DT) classification. The GAN module effectively removes noise from agricultural images, enhancing quality and stability under challenging imaging conditions. Following denoising, a combination of color, texture, and gradient features is extracted to obtain rich and discriminative patterns, which are then used to train a DT classifier for disease identification. Experiments are conducted on benchmark datasets comprising rice and cotton leaf images. The proposed system achieves superior performance, with 98.70% accuracy, 98.20% precision, 97.22% recall, and 98.50% F1 score, outperforming existing methods. These results demonstrate that the GAN-based denoising approach, combined with traditional feature-based classification, offers a robust, efficient, and practical solution for modern agricultural disease monitoring systems.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Venkata Seshukumari, Bandreddi; Tayi, Jyothirmayi; Bhuthkuri, Rajeshkhanna; Madireddy, Bhavani; Yellapu, Jhansi; Rajanna, Bodapati Venkata; Kolukula, Nitalaksheswara Rao; Kodali, Siva Sairam Prasad; Pinajala, Jayasree; Meka, James Stephen; Rami Reddy, Chilakala
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.pp106-115

Abstract

Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Comparison of image enhancement methods for pratima theft detection using artificial intelligence Sudarma, Made; Ariyani, Ni Wayan Sri; Udayana, I Putu Agus Eka Darma; Pranatayana, Ida Bagus Gde; Jasa, Lie
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.pp213-228

Abstract

The theft of pratima in Balinese temples threatens the spiritual and cultural balance of the community. These sacred objects, regarded as manifestations of God in Hinduism, hold profound religious significance, and their loss represents both material and spiritual desecration. To address this issue, this study investigates a security system that leverages image enhancement for low-light detection. Four techniques—contrast limited adaptive histogram equalization (CLAHE), adaptive histogram equalization (AHE), histogram equalization (HE), and gamma correction—were evaluated to improve image quality. CLAHE yielded the lowest mean squared error (MSE) of 21.16 and the highest peak signal-to-noise ratio (PSNR) of 38.13 dB. For object detection, VGG-19 and AlexNet were assessed. The best configuration, VGG-19 with HE, reached 83.33% accuracy and 93.75% recall, and achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.90±0.02 across five runs. Thresholds derived from the ROC analysis were selected using the Youden J statistic to balance sensitivity and specificity. The approach outperformed lightweight and classical baselines in AUC, indicating superior discrimination under low illumination. These findings show that superior image quality does not always align with higher detection accuracy, and they highlight the importance of pairing effective enhancement with robust detectors for temple security. The study contributes practical insights for preserving Balinese cultural and spiritual heritage by strengthening efforts to protect pratima against theft.
Deformable spatial pyramid pooling-enhanced EfficientNet with weighted feature fusion for pomegranate fruit disease diagnosis Bommenahalli Mallikarjunaiah, Harish; Baluvaneralu Veeranna, Balaji Prabhu
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.pp642-654

Abstract

Pomegranate is a fruit of high nutritional and economic importance. Still, it is highly susceptible to different diseases during its growing stages, leading to significant yield losses and financial setbacks for farmers. This article proposes a novel disease detection model that integrates handcrafted features with deep features extracted using a developed deformable spatial pyramid pooling (DSPP)-EfficientNet architecture. Handcrafted features such as color (RGB and HSV histograms), texture features from gray level co occurrence matrix (GLCM), and shape attributes extracted from contour descriptors and Hu moments are captured and fused with deep features by weighted fusion strategy, resulted in the most discriminative information. The fused features are categorized using a support vector machine (SVM) in a classification phase, which effectively classifies different classes of pomegranate fruit diseases. The combined deep and handcrafted features obtained 96.66% accuracy, 96.26% precision, 96.50% recall, 96.37% F1 score, and 95.64% specificity on the pomegranate fruit disease dataset which compared to existing techniques.
Development of generalized principal component analysis using multiple imputation genetic algorithm Zubedi, Fahrezal; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Syafitri, Utami Dyah
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.pp454-468

Abstract

In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.
Hybrid convolutional networks, hidden Markov models, and autoencoders for enhanced recognition Naji, Driss; Elhattab, Kamal; Joumad, Abdelali; Ait Ider, Abdelouahed; Ouisaadane, Abdelkbir; Idhmad, Azzeddine
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.pp780-787

Abstract

Recognition problems, including object detection, scene understanding, and fine-grained categorisation, are popular subjects in computer vision. However, it is challenging to model spatial coherence and contextual dependencies in response to changes in configurations. Human Vs computers' ability in perception-although convolutional neural networks (CNNs) do well in the extraction of features, they have high dependence on local receptive fields and are not able to capture long-range spatial relationships and high-order interactions. To alleviate the shortcomings of the current approaches, we present an enhanced hybrid CNNs two dimensional hidden Markov model (2D-HMM) framework that combines 2D-HMM, Markov random fields (MRF) and variational autoencoders (VAEs) into a single model. The model employs 2D-HMMs for pairwise spatial modelling, MRFs for higher order context, and VAEs for stable latent representation learning. Tested on the MNIST and CIFAR-10 benchmark datasets, our approach consistently outperforms the state-of-the-art performance by 98.2% and 89.5%, respectively, with high robustness to noise and occlusion. Results from ablation studies further show that MRFs improve recall by 1.6% and VAEs improve precision by 1.3%, suggesting that they complement each other sufficiently with respect to overall testing performance. This work unifies deep learning and probabilistic graphical models, leading to more interpretable, scalable, and accurate recognition systems.
Efficient YOLO-based models for real-time ceramic crack detection Maungmeesri, Benchalak; Khonthon, Sasithorn; Maneetham, Dechrit; Crisnapati, Padma Nyoman
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.pp852-860

Abstract

The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.
Intelligent plant disease detection using twin attention optimal convolutional neural network Pai, Prameetha; S. J., Namitha; T., Sowmya; S., Amutha; Gondi, Nisarga
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.pp756-765

Abstract

Farming is one of the most important ways for people in India to make a living. Rice is a staple food, and when farmers successfully harvest rice crops, pests often attack them, which costs agriculture a lot of money. There are now a lot of new AI-based ways to help with this problem in rice plants. But those ways don’t work very well because they take a long time and make mistakes when sorting things. This article talks about a new hybrid deep learning (DL) method for finding leaf diseases in rice plants. This process has four main steps: pre-processing, segmentation, feature extraction, and classification. A hybrid DL-based twin attention convolutional neural network (CNN) model classifies segmented images into healthy and unhealthy leaves. But this method has the problem of overfitting. An optimization method based on chaotic slime mould (CSM) solves this problem. The proposed method is compared with bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), deep neural network (DNN), and deep belief network (DBN). The suggested method has an overall accuracy of 99.56%, an F-measure of 99.21%, a sensitivity of 99.16%, a specificity of 98.56%, a precision of 99.26%, a mean absolute error (MAE) of 0.004, a mean squared error (MSE) of 0.004, and a root mean square error (RMSE) of 0.06.
Dynamic attack pattern-aware intelligent cyber-physical intrusion detection system for internet of things-edge networks Lakshminarayanappa, Vishala Ibasapura; M. Ravikumar, Kempahanumaiah
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.pp580-591

Abstract

The proliferation of Internet of Things (IoT) technologies, coupled with the convergence of edge computing infrastructures, has revolutionized modern cyber-physical systems (CPS). However, the inherently distributed architecture of these systems increases their vulnerability to advanced network-level cyber threats, posing significant challenges to data integrity and system reliability. Traditional machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS) often fall short in identifying evolving attack vectors due to their limited adaptability. To address these limitations, this paper introduces a novel Dynamic Attack Pattern-Aware Improvised Weighted Gradient Boosting (DAPA-IWGB) model designed to enhance real-time threat detection and adaptive response within IoT-edge-enabled CPS environments. The DAPA-IWGB framework synergizes gradient tree boosting with an enhanced loss function handling covariate shift, while incorporating statistical monitoring mechanisms for dynamic covariate shift recognition and continuous learning. Comprehensive experimental validation using two prominent benchmark datasets ToN-IoT and UNSW-NB15 demonstrates the proposed model’s robustness and superior performance, achieving detection accuracies of 99.921% and 99.93%, respectively. Comparative evaluations highlight substantial improvements in detection accuracy, adaptability, and reliability over existing IDS solutions. The results affirm the effectiveness of the DAPA-IWGB model in fortifying the security posture of distributed IoT-based CPS against sophisticated and evolving cyber threats.
Air quality prediction using boosting-based machine learning models for sustainable environment Fauzi, Ahmad; Maharina, Maharina; Indra, Jamaludin; Ratna Juwita, Ayu; Hananto, Agustia; Nurlaelasari, Euis
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.pp515-523

Abstract

High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.

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