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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
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Kannada handwritten numeral recognition through deep learning and optimized hyperparameter tuning S., Ujwala B.; S., Pramod Kumar; Mahadevaswamy, H. R.; K., Sumathi
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.pp5038-5048

Abstract

The classification of handwritten numerals is a vital and challenging task in developing automated systems, including postal address sorting and license plate recognition. The present study elucidates a new methodology for recognizing Kannada handwritten numerals using deep learning ResNet and VGG architecture with transfer learning. The challenge in Kannada handwritten recognition is complicated structural hierarchy and large vocabulary. The major problem in deep neural networks is vanishing gradient, which can lead to degradation in character recognition, and was addressed using our new methodology using ResNet architecture. We apply the proposed ResNet method in various real-world applications and compare it with convolutional neural networks (CNN) architecture, VGG. The experiment was implemented with the Google Colab software version on a self-created dataset, with handwritten Kannada numerals fed as the input to the recognition process. Our proposed method achieved a high accuracy of 99.20% on training samples and a generalization accuracy of 97.5% on test samples, indicating our method's effectiveness in recognizing handwritten Kannada numerals.
A blended ensemble approach for accurate human activity recognition Karim, Rezwana; Begum, Afsana; Jannat, Miskatul; Bitto, Abu Kowshir
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.pp5131-5139

Abstract

Human activity recognition (HAR) is a novel computer vision area with applications in fashion, entertainment, healthcare, and urban planning. Previously, convolutional neural networks (CNNs) were used in HAR due to their ability to extract spatial features from images. However, CNNs are not effective in processing varying input sizes and long-range dependencies in complex human motions. This work examines another approach using vision transformers (ViT) and swin transformers (SwinT) that process images as patch sequences and perform self-attention. These models particularly excel in learning global relationships and minor motion changes in body motion and are therefore very well-suited to variegated and subtle activity detection. To further enhance recognition performance, we propose a hybrid ensemble method by combining ViT and SwinT models with different scales (small, base, and large). Experimental outcomes show that while single transformer models are competitive, the hybrid ensemble beats them across the board with the highest accuracy and balanced precision, recall, and F1-score. These findings confirm that the intended ensemble model provides a more scalable and robust solution than either single-model or CNN-based approaches, and this encourages accurate human activity recognition.
Dynamic service-aware network selection framework for multi objective optimization in 5G-advanced heterogeneous wireless networks Srinivas, Bhavana; Uma Reddy, Nadig Vijayendra
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.pp4993-5007

Abstract

The increasing complexity of heterogeneous wireless networks (HWNs) and the diverse requirements of mobility patterns and service classes necessitate advanced solutions for network selection and resource optimization. Existing models often fall short in addressing dynamic mobility scenarios and service differentiation, leading to inefficiencies in resource allocation, suboptimal throughput, and increased latency. To overcome these limitations, this study proposes a dynamic service-aware network selector (DSANS) framework for 5G-advanced environments. The framework integrates an adaptive deep decision network (ADDN) for multi-objective optimization, addressing critical quality of service (QoS) metrics such as throughput, delay, and energy efficiency while enhancing quality of experience (QoE) for applications like enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and internet of things (IoT). The DSANS framework dynamically adapts to mobility patterns and varying network conditions, ensuring efficient resource estimation and optimal network selection. Simulation results highlight its superiority, achieving up to 25% improvement in throughput and a 15% reduction in latency compared to state-of-the-art algorithms. These findings validate DSANS as a robust solution for mitigating the limitations of existing models, optimizing network performance, and meeting the stringent demands of next-generation HWNs.
Hybrid N-gram-based framework for payload distributed denial of service detection and classification Maslan, Andi; Mohd Foozy, Cik Feresa; Bin Mohamad, Kamaruddin Malik; Hamid, Abdul; Fitriawan, Dedy; Hasugian, Joni
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.pp4763-4774

Abstract

There are three primary approaches to DDoS detection: anomaly-based, pattern-based, and heuristic-based. The heuristic-based method integrates both anomaly- and pattern-based techniques. However, existing DDoS detection systems face challenges in performing HTTP payload-level analysis, mainly due to high false positive rates and insufficient granularity in current datasets. To address this, the study introduces a novel heuristic approach based on a hybrid N-Gram model. This hybrid combines two components: CSDPayload+N-Gram and CSPayload+N-Gram. CSDPayload represents the gap (measured via Chi-Square Distance) between a given payload and normal traffic payloads, while CSPayload reflects the similarity (measured via Cosine Similarity) between them. These metrics form a new feature set evaluated using three datasets: CIC2019, MIB2016, and H2N-Payload. The methodology begins with packet extraction and conversion of TCP/IP traffic—specifically HTTP traffic—into hexadecimal payloads. N-Gram analysis (from 1-Gram to 6-Gram) is then applied to these payloads. For each N-Gram, frequency counts are computed, followed by calculations of Chi-Square Distance (CSD), Cosine Similarity (CS), and Pearson’s Chi-Square test to classify payloads as either benign or malicious. Subsequently, feature selection is performed using weight correlation, and the resulting features are fed into three machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network. Experimental results demonstrate high detection accuracy, particularly in the 4-Gram feature category: Neural Network achieves 99.65%, KNN 95.14%, and SVM 99.73% accuracy on average.
Comparative evaluation of machine learning models for intrusion detection in WSNs using the IDSAI dataset Lmkaiti, Mansour; Moudni, Houda; Mouncif, Hicham
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.pp4913-4922

Abstract

This paper provides comparative assessment of three lightweight machine learning (ML) models (logistic regression (LR), random forest (RF), and gradient boosting (GB)), which are employed to detect intrusions in wireless sensor networks (WSNs) using the IDSAI dataset. The goal is to determine the most effective and deployable classifier within the constraints of WSN resources. In order to prevent data leakage and report accuracy, precision, recall, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) with mean±SD, we implement stratified 5-fold cross validation with in fold preprocessing. The results indicate that RF provides the most optimal generalization and overall performance (accuracy 0.9994 ± 0.0001, precision 0.9995±0.0001, recall 0.9994±0.0001, F1-score 0.9994±0.0001, ROC–AUC 0.9998 ± 0.0000). RF is closely followed by GB (accuracy 0.9990±0.0001, precision 0.9995±0.0001, recall 0.9985±0.0001, F1-score 0.9990 ± 0.0001, ROC-AUC ≈ 1.0000). LR demonstrates limitations in linearly overlapping classes, as evidenced by its high precision but reduced recall (accuracy 0.9167±0.0010, precision 0.9829±0.0002, recall 0.8481±0.0018, F1-score 0.9105 ± 0.0011, ROC–AUC 0.9707 ± 0.0001). In order to evaluate deployability, we characterize the inference throughput on a modest PC: LR ∼ 6.5 × 105 samples/s, GB ∼ 2.2 × 105 samples/s, and RF ∼ 1.3 × 105 samples/s, indicating a tiered intrusion detection system (IDS) (LR at sensors, RF at cluster-heads, and GB at the gateway). We also address the potential dangers of overfitting that may arise from the cleanliness of the dataset and provide a roadmap for future validation on a more diverse set of traffic. The research establishes a baseline for lightweight IDS in actual WSNs that is deployable and reproducible.
Melanoma classification using ensemble deep transfer learning Gadag, Soumya; Vishwanathac, Panduranga Rao Malode; Dalal, Virupaxi Balachandra
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.pp4943-4956

Abstract

Melanoma, a type of skin cancer, poses significant challenges in early detection and diagnosis. Several methods for early melanoma detection, including visual inspection and several machine learning models, face challenges with accuracy. To overcome these issues, deep learning has been widely adopted in various biomedical applications. In this work, we employ deep transfer learning methods to classify melanoma. Firstly, we collect publicly available datasets containing melanoma images, their corresponding ground truth for segmentation, and class labels. Subsequently, we perform data preprocessing, normalization, and label encoding to address issues of varied illumination, image noise, and data imbalance. Next, we conduct feature extraction utilizing the previously trained deep learning models, VGG, ResNet, InceptionResNet, and MobileNet. The characteristic vectors obtained from each model are fused to produce a comprehensive depiction among the provided pictures. In the classification stage, we employ ensemble learning using transfer learning models, including EfficientNet, Xception, and DenseNet. These models are trained on the final feature vector to classify melanoma images effectively. The effectiveness of the suggested method is verified using publicly available ISIC 2017–2020 datasets, these model reports average accuracy scores of 96.10%, 97.23%, 97.50%, 98.33%, and 98.60%, in that order.
Classification of regional language dialects using convolutional neural network and multilayer perceptron Marasabessy, Fahmi B.; Riana, Dwiza; Ernawati, Muji
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.pp5017-5026

Abstract

Regional languages are vital for communication and preserving cultural identity, safeguarding local heritage. However, globalization and modernization endanger their existence as they are increasingly replaced by national or global languages. Despite progress in dialect recognition research, particularly for certain languages, further studies are needed to improve model performance and address less-represented dialects, including those in Indonesia. This study enhances a custom-built dataset for dialect recognition through the application of data augmentation techniques, specifically adding noise, time stretching, and pitch shifting. Using Mel-frequency cepstral coefficients (MFCC) for feature extraction, it evaluates the performance of convolutional neural network (CNN) and multilayer perceptron (MLP) in classifying six Indonesian dialects. Results indicate that CNN outperformed, achieving 97.92% accuracy, 97.90% recall, 97.97% precision, 97.92% F1-score, and a kappa score of 97.49% with combined augmentation techniques, setting a foundation for further research.
Optimizing sparse ternary compression with thresholds for communication-efficient federated learning Murthy Chittaiah, Nithyanianjan; Haladappa, Manjula Sunkadakatte
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.pp4902-4912

Abstract

Federated learning (FL) enables decentralized model training while preserving client data privacy, yet suffers from significant communication overhead due to frequent parameter exchanges. This study investigates how varying sparse ternary compression (STC) thresholds impact communication efficiency and model accuracy across the CIFAR-10 and MedMNIST datasets. Experiments tested thresholds ranging from 1.0 to 1.9 and batch sizes of 10, 15, and 20. Results demonstrated that selecting thresholds between 1.2 and 1.5 reduced total communication costs by approximately 10–15%, while maintaining acceptable accuracy levels. These findings suggest that careful threshold tuning can achieve substantial communication savings with minimal compromise in model performance, offering practical guidance for improving the efficiency and scalability of FL systems.
Hybrid AI framework for anomaly detection and root cause analysis in multi-agent systems Rachid, Tahri; Abdellah, Ouammou; Abdellatif, Lasbahani; Jarrar, Abdessamad; Youssef, Balouki
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.pp5290-5302

Abstract

Anomaly detection and root cause analysis (RCA) are critical for securing intelligent systems against evolving threats. Traditional models often suffer from high false alarms, weak adaptability to streaming contexts, and limited interpretability. This work proposes a hybrid artificial intelligence (AI) framework that integrates machine learning (ML) with prior knowledge, semantic rules, and bio-inspired modeling. The approach strengthens detection of diverse attacks, including DoS/DDoS, Probe, U2R, and R2L, while reducing human intervention. Experiments on the NSL-KDD dataset demonstrate that our method decreases spurious alerts by up to 90%, improves accuracy by 2–4%, and reduces false positives/negatives by about 4%. Beyond statistical gains, the framework ensures robustness in real-time environments, offering interpretable and scalable anomaly detection for heterogeneous systems. These results highlight the potential of hybrid symbolic–subsymbolic AI to enhance reliability in next-generation security infrastructures.
Arabic text classification using machine learning and deep learning algorithms Alqahtani, Rawad Awad; Abdelhafez, Hoda A.
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.pp5201-5217

Abstract

The classification of Arabic textual content presents considerable challenges due to the language's rich morphological structure and the wide variation among its dialects. This study aims to enhance classification accuracy by leveraging ensemble learning techniques and a deep bidirectional transformer-based model, specifically the multilingual autoregressive BERT (MARBERT). To address linguistic variability, advanced preprocessing techniques were employed, including Farasa, Tashaphyne, and Assem stemming methods. The Al Khaleej dataset served as the basis for supervised learning, providing a representative sample of Arabic text. Furthermore, term frequency-inverse document frequency (TF-IDF) with bigram and trigram feature extraction was utilized to effectively capture contextual semantics. Experimental results indicate that the proposed approach, particularly with the integration of MARBERT, achieves a peak classification accuracy of 98.59%, outperforming existing models. This research underscores the efficacy of combining ensemble learning with deep transformer-based models for Arabic text classification and highlights the critical role of robust preprocessing techniques in managing linguistic complexity and improving model performance.

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