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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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Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems Krishnamoorthy, Latha; Raju, Ammasandra Sadashivaiah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3160-3171

Abstract

Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Personalized virtual reality therapy for children with autism spectrum disorder Belmaqrout, Ahlam; El Ghali, Btihal; Daoudi, Najima; Haqiq, Abdelhay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3444-3451

Abstract

The treatment of autism spectrum disorders (ASD) has often relied on broad therapeutic approaches that may not meet each individual's specific needs. This research highlights the importance of personalized therapy to address the unique sensory and emotional requirements of autistic children. We explore recent advances in therapeutic technologies, focusing on serious games and virtual reality (VR) as promising tools in this field. Our proposed solution is a VR application designed to provide a personalized, relaxing experience for children with autism. The application is tailored to accommodate individual preferences and sensory sensitivities, adjusting visual and auditory stimuli to reduce sensory overload and promote emotional regulation. This personalized approach aims to help children manage anxiety and stress more effectively.
A novel fuzzy logic based sliding mode control scheme for non-linear systems Kareem, Abdul; Kumara, Varuna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2676-2688

Abstract

Sliding mode control (SMC) has been widely used in the control of non-linear systems due to many inherent properties like superposition, multiple isolated equilibrium points, finite escape time, limit cycle, bifurcation. This research proposes super-twisting controller architecture with a varying sliding surface; the sliding surface being adjusted by a simple single input-single output (SISO) fuzzy logic inference system. The proposed super-twisting controller utilizes a varying sliding surface with an online slope update using a SISO fuzzy logic inference system. This rotates sliding surface in the direction of enhancing the dynamic performance of the system without compromising steady state performance and stability. The performance of the proposed controller is compared to that of the basic super-twisting sliding mode (STSM) controller with a fixed sliding surface through simulations for a benchmark non-linear system control system model with parametric uncertainties and disturbances. The simulation results have confirmed that the proposed approach has the improved dynamic performance in terms of faster response than the typical STSM controller with a fixed sliding surface. This improved dynamic performance is achieved without affecting robustness, system stability and level of accuracy in tracking. The proposed control approach is straightforward to implement since the sliding surface slope is regulated by a SISO fuzzy logic inference system. The MATLAB/Simulink is used to display the efficiency of proposed system over conventional system.
Imagery based plant disease detection using conventional neural networks and transfer learning Mhaned, Ali; Mouatassim, Salma; El Haji, Mounia; Benhra, Jamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2701-2712

Abstract

Ensuring the sustainability of global food production requires efficient plant disease detection, challenge conventional methods struggle to address promptly. This study explores advanced techniques, including convolutional neural networks (CNNs) and transfer learning models (ResNet and VGG), to improve plant disease identification accuracy. Using a plant disease dataset with 65 classes of healthy and diseased leaves, the research evaluates these models' effectiveness in automating disease recognition. Preprocessing techniques, such as size normalization and data augmentation, are employed to enhance model reliability, and the dataset is divided into training, testing, and validation sets. The CNN model achieved accuracies of 95.45 and 94.52% for 128×128 and 256×256 image sizes, respectively. ResNet50 proved the best performer, reaching 98.38 and 98.63% accuracy, while VGG16 achieved 97.99 and 98.34%. These results highlight ResNet50's superior ability to capture intricate features, making it a robust tool for precision agriculture. This research provides practical solutions for early and accurate disease identification, helping to improve crop management and food security.
Optimized data security and storage using improved blowfish and modular encryption in cloud-based internet of things Guddappa, Saritha Ibakkanavar; Shivakumaraswamy, Sowmyashree Malligehalli; Guddappa, Naveen Ibakkanavar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2667-2675

Abstract

The increasing development of the internet of things (IoT) has made cloud-based storage systems essential for storing, processing, and sharing IoT data. Ensuring cloud security is crucial as it manages a large volume of sensitive and outsourced data vulnerable to unauthorized access. This research proposes an improved blowfish algorithm and modular encryption standard (IBA-MES) for secure and efficient data storage in cloud-based IoT systems. The block cipher structure in IBA enables scaling for different data sizes, ensuring secure data handling across a wide range of IoT devices. Additionally, IBA-MES adaptability helps maintain data integrity, enhancing both the security and efficiency of data storage in cloud-based IoT environments. Modular encryption standard (MES) reduces latency during encryption operations, ensuring quick data transactions between the cloud server and IoT devices. By combining blowfish’s speed and strength with modular encryption’s adaptability, IBA-MES provides robust data protection. Metrics such as execution time, central processing unit (CPU) usage, encryption time, decryption time, runtime, and latency are calculated for the proposed IBA-MES. For 700 blocks, the IBA-MES achieves encryption and decryption times of 270 and 415 ms, respectively, outperforming the triple data encryption standard (TDES).
Dual simulated annealing soft decoder for linear block codes Alaoui, Hicham Tahiri; Azouaoui, Ahmed; El Kafi, Jamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2776-2787

Abstract

This paper proposes a new approach to soft decoding for linear block codes called dual simulated annealing soft decoder (DSASD) which utilizes the dual code instead of the original code, using the simulated annealing algorithm as presented in a previously developed work. The DSASD algorithm demonstrates superior decoding performance across a wide range of codes, outperforming classical simulated annealing and several other tested decoders. We conduct a comprehensive evaluation of the proposed algorithm's performance, optimizing its parameters to achieve the best possible results. Additionally, we compare its decoding performance and algorithmic complexity with other decoding algorithms in its category. Our results demonstrate a gain in performance of approximately 2.5 dB at a bit error rate (BER) of 6×10⁻⁶ for the LDPC (60,30) code.
Evaluating the influence of feature selection-based dimensionality reduction on sentiment analysis Kishore, Gowrav Ramesh Babu; Harish, Bukahally Somashekar; Roopa, Chaluvegowda Kanakalakshmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3366-3374

Abstract

As social media has become an integral part of digital medium, the usage of the same has increased multi-fold in recent years. With increase in usage, the sentiment analysis of such data has emerged as one of the most sought research domains. At the same time, social media texts are known to pose variety of challenges during the analysis, thus making pre-processing one of the important steps. The aim of this work is to perform sentiment analysis on social media text, while handling the noise effectively in the data. This study is performed on a multi-class twitter sentiment dataset. Firstly, we apply several text cleaning techniques in order to eliminate noise and redundancy in the data. In addition, we examine the influence of regularized locality preserving indexing (RLPI) technique combined with the well-known word weighting methods. The findings obtained from experiment indicate that, RLPI outperforms other algorithms in feature selection and when paired with long short-term memory (LSTM), the combination outperforms other classification models that are discussed.
Broiler meats tenderness prediction using near infrared spectroscopy against non-linear predictive modelling Ghazali, Rashidah; Abdul Rahim, Herlina; Nurani Zulkifli, Syahidah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2713-2723

Abstract

Near infrared (NIR) spectroscopy is a non-invasive analytical technique known for its ability to assess the quality attributes of meat products. However, the linear models utilized, partial least square (PLS) and principal component regression (PCR) achieved unsatisfactory performances of meat physical attributes prediction. Hence, in this research, for its inherent advantages in modelling nonlinear system, artificial neural network (ANN) is augmented to the components of PCR and PLS. Through the augmentation, the principal component neural network (PCNN) and latent variable neural network (LVNN) models are developed. From the results obtained, it shows that PCNN and LVNN successfully surpassed their respective linear versions of PCR and PLS by 70% higher shear force prediction performances. The LVNN proved to achieve the best prediction in breast meat with root mean square error of prediction (RMSEP) of 0.0769 kg and coefficient of determination (RP2) of 0.8201 whilst for drumsticks, RMSEP=0.1494 kg and RP2=0.8606. NIR spectroscopy technology integrated with machine learning yields a promising non-invasive technique in predicting the shear force of intact raw broiler meat.
Strid-CNN: moving filters with convolution neural network for multi-class pneumonia classification Trivedi, Khushboo; Bhupeshbhai Thacker, Chintan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3253-3261

Abstract

Millions of people around the world suffer from pneumonia, a serious lung illness. To effectively treat and manage this condition, a quick and accurate diagnosis is essential. This study thoroughly examines different ways of using transfer learning to classify pneumonia into multiple categories. We use well-known methods like DenseNet121, VGGNet-16, ResNet-50, and Inception Net, as well as a new method called Strid-CNN, which applies moving filters with convolution neural network. Through extensive testing, we show that each method effectively uses pre-learned information on a large dataset of medical images, accurately identifying pneumonia across various classes. Our results reveal subtle differences in performance among these methods, providing insights into how well they adapt to the challenging field of medical image analysis. Additionally, the Strid-CNN method shows promising results, indicating its potential as a competitive alternative. This research offers valuable guidance on choosing the right transfer learning approach for classifying pneumonia into multiple categories, contributing to improvements in diagnostic accuracy and healthcare effectiveness. Our study not only highlights the current state of transfer learning in pneumonia classification but also its potential to enhance clinical outcomes and patient care.
Classification of Tasikmalaya batik motifs using convolutional neural networks Mufizar, Teuku; Sudiarjo, Aso; Dewi Sri Mulyani, Evi; Ahmad Wakih, Agus; Akbar Kasyfurrahman, Muhammad; Adilal Mahbub, Luthfi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3287-3299

Abstract

This paper presents a study on the classification of traditional Tasikmalaya batik motifs using convolutional neural networks (CNN). The experiments revealed that the high complexity of batik motifs significantly impacted model performance, as the handling of each class influenced the overall results. Initial experiments with the original dataset demonstrated suboptimal performance, characterized by accuracy and validation curves indicating overfitting, with only 75% accuracy achieved at a learning rate of 0.001, a batch size of 32, and 50 epochs. To enhance performance, we implemented data segmentation, data augmentation, optimized the choice of the best optimizer, utilized an optimal architecture, and conducted hyperparameter tuning. The best-performing model was trained on data subjected to specific preprocessing for each class, using the Adam optimizer with hyperparameter tuning set to a learning rate of 0.001, a batch size of 32, and 50 epochs. In the hyperparameter tuning experiment with the visual geometry group network (VGGNet) architecture, it was shown that there is an improvement in the prediction of the kumeli class, achieving an accuracy of 100%.

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