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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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Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks Khaleel, Maha Ibrahim; Lakizadeh, Amir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2291-2301

Abstract

As a variant of skin cancer, melanoma represents a substantial menace to the health and overall well-being of individuals. Statistics reveal that 55% of skin cancer patients succumb to this particular disease. However, early detection plays a crucial role in reducing mortality rates and saving lives. In the past several decades, there has been a rise in the adoption of deep learning algorithms, capturing the interest of researchers working in this field. One popular method involves utilizing pre-trained deep neural networks. In this study, a hybrid approach is employed to extract features from melanoma images. This approach integrates the utilization of pre-trained architectures, including AlexNet, ResNet-50, and GoogleNet. During the transfer training phase, these networks are fine-tuned to detect skin cancer by adjusting the learning rate. Subsequently, the maximum relevance minimum redundancy (MRMR) algorithm is employed to select optimal features based on the concepts of minimum redundancy and maximum relevance in order to minimize feature redundancy and enhance classification accuracy. The bagging technique is employed for the classification of various skin cancer types. The experimental results demonstrate the success of the suggested approach, yielding 98.9% accuracy. Furthermore, the results indicate the superiority of this method according to precision, recall, and F1-score in comparison with existing algorithms.
Convolutional neural network based encoder-decoder for efficient real-time object detection Rajasekaran, Mothiram; Sabapathy Ranganathan, Chitra; Mohankumar, Nagarajan; Sampathrajan, Rajeshkumar; Merlin Inbamalar, Thayalagaran; Nandhini, Nageshvaran; Sujatha, Shanmugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1960-1967

Abstract

Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Autism spectrum disorder classification using machine learning with factor analysis Devidas Nayak, Disha; Shedole, Seema; Mathur, Archana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2185-2195

Abstract

Due to the complexity and heterogeneity of autism spectrum disorder (ASD), diagnosis and categorization have attracted a lot of interest. To improve the robustness of ASD classification across the toddler age group, this work proposes an integrated strategy that integrates machine learning approaches with factor analysis and correlation validation. Benchmark dataset representing toddlers used to test this strategy’s efficiency. To first find the latent variables behind the ASD features in each dataset, factor analysis is used. We intend to capture the shared variance between variables and lower the dimensionality of the initial feature space by identifying these latent components. The subsequent machine-learning classification models used the retrieved components as input features. To validate the categorization results, correlation analyses were carried out in addition to factor analysis. The associations between the latent components discovered by factor analysis and the diagnostic labels were examined using Pearson correlation, a measure of linear association. The results highlight the method’s potential to improve diagnostic precision and shed light on the intricate connections between characteristics and diagnostic labels on the autism spectrum for toddlers.
Detection of partially occluded area in face image using U-Net model Cherapanamjeri, Jyothsna; Rao, Bangole Narendra Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1863-1869

Abstract

Occluded face recognition is important task in computer vision. To complete the occluded face recognition efficiently, first we need to identify the occluded region in face. Identifying the occluded region in face is a challenging task in computer vision. One case of face occlusion is nothing but wearing masks, sunglasses, and scarves. Another case of face occlusion is face is hiding the other objects like books, things, or other faces. In our research, identifying the occluded area which is corona virus disease of 2019 (COVID-19) masked area in face and generate segmentation map. In semantic segmentation, deep learning-based techniques have demonstrated promising outcomes. We have employed one of the deep learning-based U-Net models to generate a binary segmentation map on masked region of a human face. It achieves reliable performance and reducing network complexity. We train our model on MaskedFace-CelebA dataset and accuracy is 97.7%. Results from experiments demonstrate that, in comparison to the most advanced semantic segmentation models, our approach achieves a promising compromise between segmentation accuracy and computing efficiency.
Enhancing precision medicine in neuroimaging: hybrid model for brain tumor analysis Sajjanar, Ravikumar; D. Dixit, Umesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2196-2209

Abstract

Brain tumors are a significant health challenge requiring precise diagnostic methods for optimal patient care. This study introduces a novel approach utilizing a convolutional neural network-based gated recurrent unit (CNN-GRU) for brain tumor detection. The method encompasses a rigorous preprocessing pipeline tailored for multi-modal magnetic resonance imaging (MRI) images, focusing on standardizing dimensions, normalizing pixel values, and enhancing contrast to facilitate robust tumor identification. Subsequently, temporal sequences of preprocessed images are analyzed by the CNN-GRU network to accurately pinpoint tumor regions. Evaluation on the BraTS2020 dataset, comprising diverse MRI scans with manual annotations, demonstrates the method's robust performance in tumor detection, reflecting real-world clinical complexities. Through meticulous preprocessing and model optimization, the approach achieves a remarkable accuracy rate of 99%, offering crucial insights for clinicians in treatment planning and prognosis prediction. Implemented using Python, the framework contributes to advancing brain tumor diagnosis and decision support systems, potentially enhancing personalized medicine and clinical practice. By improving diagnostic accuracy and patient outcomes, this research underscores the importance of integrating advanced computational techniques with medical imaging to address critical healthcare challenges effectively.
Electrocardiogram sequences data analytics and classification using unsupervised and supervised machine learning algorithms Ghnimi, Sami; Raju Moola, Pratapa; Abdul Shariff, Jamaludeen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2055-2071

Abstract

This paper explores the prediction of cardiovascular disease (CVD) through the classification of electrocardiogram (ECG) sequences using both supervised and unsupervised machine learning (ML) algorithms. ECG 5000 dataset is considered to perform essential data analytics, clustering, and classification, effectively categorizing ECG heartbeats into optimal groups to forecast CVD. The Elbow and Silhouette methods are applied to estimate optimal number of clusters within the dataset. Using K-means and hierarchical clustering algorithms, the data is grouped into two and five distinguishable clusters, with performance metrics indicating that two clusters are more viable. Subsequently, multiple supervised ML classifiers—including kernel classifiers, support vector machine (SVM), naïve Bayes (NB), decision trees (DT), k-nearest neighbor (KNN) and neural networks (NN)—are trained on the labeled and clustered datasets to ensure accurate classification of ECG sequences and anomaly detection. A novel modified ML classifier, kernel-SVM with Chi-Square (χ²) feature selection, is introduced and demonstrates exceptional performance, achieving an impressive accuracy of 0.9848, recall of 0.9973, and a training time of 1.6944 seconds, surpassing benchmarks from prior research. The results and discussion section includes a comparison of various algorithm performances, affirming that the proposed approach is an alternative to the complex deep learning (DL) and transformer-based models.
Advancing precision in air quality forecasting through machine learning integration Komarudin, Muhamad; Ratna Sulistiyanti, Sri; Suharso, Suharso; Irsyad, Muhammad; Dian Septama, Hery; Yulianti, Titin; Sophian, Ali; Michel, Michel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2113-2122

Abstract

In an era where environmental concerns are escalating, air quality forecasting emerges. Forecasting is a crucial tool for addressing the adverse impacts of pollution on public health and ecosystems. In urban centers like Bandar Lampung, economic activities intensify pollution levels. This condition leveraging advanced machine learning forecasting methods can significantly mitigate these effects. This study evaluates the precision of long short-term memory (LSTM) and Prophet methods in predicting air quality. This study utilizes data from January 12, 2022 to November 9, 2023. The results reveal a distinct advantage of the LSTM method over the Prophet. The LSTM method showcases superior accuracy across all evaluation metrics. Specifically, the LSTM method achieved an average root mean squared error (RMSE) of 5.38, mean absolute error (MAE) of 3.94, and mean absolute percentage error (MAPE) of 0.07. In contrast, the Prophet method recorded higher error rates, with an average RMSE of 18.48, MAE of 15.61, and MAPE of 0.25. These numbers underscore the LSTM method's robustness and reliability in forecasting air quality. The result highlights its potential as a pivotal resource for environmental monitoring and policymaking to safeguard public health and promote sustainable urban development.
Supply chain efficiency transformation: analysis of raw material staff selection based on preference selection index Amrullah, Amrullah; Idaman, Akbar; Al-Khowarizmi, Al-Khowarizmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2459-2470

Abstract

In the era of intense business globalization, supply chain management is becoming a vital key to improving the efficiency and competitiveness of enterprises. The selection of raw material supply staff is an important aspect of supply chain management, affecting smooth supply, efficiency and cost control. This research focuses on using the preference selection index (PSI) method in the selection of raw material supply staff. PSI is a tool that integrates data from multiple criteria in the selection process. The results show that PSI provides an effective evaluation in staff selection, identifies key variables that affect selection success and analyzes the impact of using PSI on supply chain efficiency and company productivity. This research fills the knowledge gap in the application of PSI in the context of raw material supply staff selection and contributes to the understanding of efficient and sustainable supply chain management. The results provide valuable insights for industries and organizations that depend on reliable raw material supply and demonstrate the potential to improve the overall staff selection process. The outcome of this study found that Muliyono received a PSI score of 0.9643 and was ranked first, while Ramli received a PSI score of 0.9548 and was ranked second.
CycleGAN for day-to-night image translation: a comparative study Raihan Taufiq, Muhammad Feriansyah; Rahadianti, Laksmita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2347-2357

Abstract

Computer vision tasks often fail when applied to night images, because the models are usually trained using clear daytime images only. This creates the need to augment the data with more nighttime image for training to increase robustness. In this study, we consider day-to-night image translation using both traditional image processing approaches and deep learning models. This study employs a hybrid framework of traditional image processing followed by a CycleGANbased deep learning model for day-to-night image translation. We then conduct a comparative study on various generator architectures in our CycleGAN model. This research compares four different CycleGAN models; i.e., the orginal CycleGAN, feature pyramid network (FPN) based CycleGAN, the original U-Net vision transformer based UVCGAN, plus a modified UVCGAN with additional edge loss. The experimental results show that the orginal UVCGAN obtains an Frechet inception distance (FID) score of 16.68 and structural similarity index ´ measure (SSIM) of 0.42, leading in terms of FID. Meanwhile, FPN-CycleGAN obtains an FID score of 104.46 and SSIM score of 0.44, leading in terms of SSIM. Considering FPN-CycleGAN’s bad FID score and visual observation, we conclude that UVCGAN is more effective in generating synthetic nighttime images.
Deep lung nodule detection using multi-resolution analysis on computed tomography images Govindan, Inbasakaran; Joseph Raj, Anitha Ruth
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1989-2000

Abstract

The lung nodule must be detected early because the patient's outcome can be enhanced following the lung cancer diagnosis. The candidate research proposed a novel computer-aided detection system based on multi-resolution technique (MRT) and local Gaussian distribution (LGD) methods to accurately identify and label the lung nodules in a computed tomography (CT) screening image. The research aimed to find all the potential nodule constructs, which combined wavelet and multiscale morphological analysis and then used the LGD method to calculate the Gaussian function parameters for each image block. Subsequently, we calculated the probability that each pixel belongs to a particular institute, which shall be used to achieve lung nodule segmentation reliably. After the segmentation, the research employed a convolutional neural network (CNN) variant to improve the detection performance further. The proposed method attained an accuracy of 0.9958, a sensitivity of 0.7899, a specificity of 0.9994 and an F1-score of 0.8651. The comparison with other methods shows that the proposed method had better detection accuracy than the different methods in terms of lung nodule detection.

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