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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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Detection of vague object signatures on deep learning surveillance devices Swardika, I Ketut; Widyastuti Santiary, Putri Alit
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3262-3272

Abstract

The deep learning of object detection has become a breakthrough in recent years. Many papers demonstrated that this method records significant reliability results. However, the question arises whether objects that were successfully detected are initially conditioned clear in daylight. The object being detected is in the form of a photographic product that has numerous problems. It can be distant or have low-contrast so that their signatures are challenging to recognize, especially detection of persons in surveillance systems for dark-environments. This paper contributes to proving the deep learning method capable of detecting night-person (NP) with high precision and recall in the dark without image enhancement, by using ordinary cameras which operate on day-night or visible-near infrared spectrum runs on embedded systems. For that, an infrared-cut filter mechanical shutter is designed to block for the day or deliver infrared light for the night. The NP signatures are illuminated by an external infrared light source, providing three-channel high-resolution images. The distance of a NP from the camera becomes a decisive successful detection. The external infrared light source makes objects under or overexposed affecting the object being recognized. The validation with thoroughly new data of the NP constantly provides high precision and recall.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray images El Omary, Sara; Lahrache, Souad; El Ouazzani, Rajae
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1005-1013

Abstract

Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions.Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Hybrid adaptive neural network for remote sensing image classification Sathyanarayana, Natya; Singh, Seema
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2291-2300

Abstract

The proposed study employed a method for identifying the main contents (category/class) that a remote sensing image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution (PDD) and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward artificial neural network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.
Advanced digital competency assessment of vocational teachers': A new approach based on fuzzy-analytical hierarcy process Ramadhan Islami, Aditya; Abdullah, Ade Gafar; Widiaty, Isma; Yulia, Cica; Lukman Hakim, Dadang; Handoko, Erfan; Subekti, Eri; Rahmawati, Sherly
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2781-2795

Abstract

Teachers need digital competence to adapt easily to the current digital era. This study tries to discover the perceptions of vocational high school (VHS) teachers in Indonesia related to advanced digital competencies, which include information, communication, content creation, digital security, and problem-solving competencies. The multi-criteria Analytical Hierarchy Process (AHP) problem-solving method is used to rank the priority digital competencies that are most mastered by the respondents, and then their performance is validated by the fuzzy AHP artificial intelligence-based method. A poll was conducted with 392 respondents, with the research instrument adopting the digital competency measurement platform from DigComp. The study's results show that the fuzzy AHP method has proven that the classical AHP method is a very good way to prioritize VHS teachers' digital skills based on several factors. The two methods gave almost identical results in determining the priority order of VHS teacher digital competencies. The survey results reveal that VHS teachers in Indonesia must immediately develop their skills in terms of digital content creation and digital security. Teachers, teacher professional organizations, and decision-makers are expected to use the findings of this study as a reference in implementing VHS teacher digital competency improvement trainings.
A skeleton-based method for exercise recognition based on 3D coordinates of human joints Bilous, Nataliya; Svidin, Oleh; Ahekian, Iryna; Malko, Vladyslav
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1805-1816

Abstract

The aim of the work is to develop the method of identification and comparison of poses and exercises performed by a person that will have a low sensitivity to data errors. This method uses their formal descriptions in the form of conjunctions of logical statements and should work regardless of the shooting angle at which the video was taken and the proportions of the person on it. Each statement describes the position of the joints relative to each other along one of the axes. The joint coordinates are corrected by taking into account the length of the bones that connect them that eliminates the necessity to process outliers and it also improves the accuracy of joints positioning. Removal of errors out of the data using the method of averaging the graph along each axis at every step. In order to do this, consecutive points are grouped so that the difference between the maximum and the minimum does not exceed the error. The groups are then filtered to leave only those in which both are smaller or both are larger. The proposed method of identification requires just a modern smartphone and has no restrictions on how to take video of exercises.
Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks Nugroho, Kristiawan; Hadiono, Kristophorus; Sutanto, Felix; Marutho, Dhendra; Farooq, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2904-2911

Abstract

Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.
Dental caries detection using faster region-based convolutional neural network with residual network Lanyak, Andre Citro Febriliyan; Prasetiadi, Agi; Widodo, Haris Budi; Ghani, Muhammad Hisyam; Athallah, Abiyan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2027-2035

Abstract

Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are then augmented to a total of 486 images and annotated by dental health experts from Jenderal Soedirman University. Transfer learning using pre-trained Faster R-CNN residual network (ResNet)-50 and ResNet-101 model is utilized to detect and localise dental caries. The Faster R-CNN ResNet-50 model trained using the Adam optimizer produces a mean average precision (mAP) of 0.213, and those using the momentum optimizer produce a mAP of 0.177. While the Faster R-CNN ResNet-101 model trained using the Adam optimizer produces a mAP of 0.192, and those using the momentum optimizer produce a mAP of 0.004. The model trained on the dataset showed satisfactory results in detecting dental caries, especially ResNet-50 with Adam optimizer.
Design of novel convolution neural network model for lung cancer detection by using sensitivity maps Saxena, Sugandha; Narasimha Prasad, Sarappadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3218-3227

Abstract

Despite the existence of numerous models for detecting lung cancer, there is still room for achieving higher levels of accuracy. In this paper, a maximum sensitivity neural network (MSNN) has been proposed. As the name suggests, the model aims to achieve high sensitivity and offers a viable remedy to minimize the number of false positive in oder to improve the overall accuracy for lung cancer detection. The MSNN model is a promising model since it can efficiently interpret grayscale lung computed tomography (CT) scan images as inputs and can be trained using just a few images also. This model has surpassed previous deep learning models by obtaining a remarkable sensitivity of 94.6% and an accuracy of 96.9%. A sensitivity map is created, offering important insights into the critical regions for finding malignant nodules. This innovative method has shown outstanding performance in identifying lung cancer with a low false positive rate which can increase the accuracy of medical diagnoses.
Facial recognition based on enhanced neural network AL-Qinani, Iman Hussein; Saleh, Kawther Thabt; Saleh, Hayder Adnan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp207-216

Abstract

Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset. 
Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis Melani Puspasari, Hasna; Zharif Mustaqim, Ilham; Tri Utami, Avita; Syalevi, Rahmad; Ruldeviyani, Yova
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1596-1607

Abstract

The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.

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