cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 120 Documents
 4   5   6   7   8 
Search results for , issue "Vol 13, No 1: March 2024" : 120 Documents clear
Face and liveness detection with criminal identification using machine learning and image processing techniques for security system Shinde, Pratibha; Raundale, Ajay R.
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.pp722-729

Abstract

In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
Predicting the classification of high vowel sound by using artificial neural network: a study in forensic linguistics Susanto, Susanto; Nanda, Deri Sis
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.pp195-200

Abstract

One of the tasks in forensic linguistics, especially forensic phonetics, is evaluating the speech sounds in the recordings. The speech evaluation aims at identifying and verifying speakers to predict if the sound were spoken by the suspect or not. The common problem in the task is determining which acoustic features of the speech sounds are reliable for the speaker identification and verification. The purpose of this research is studying formant frequencies to predict high vowel sounds /i/, and /u/ by using artificial neural network (ANN). Using three various normalization methods (i.e., softmax, z-score and sigmoid), we utilized multilayer perceptron on backpropagation ANN with the architectural models of 4-5-2, 4-10-2 and 4-20-2. The results show that the z-score normalization method provides higher accuracy than the other two in all formations and the 4-10-2 formation has shown the highest accuracy (92.26%).
Signature verification based on proposed fast hyper deep neural network Hashim, Zainab; Mohsin, Hanaa; Alkhayyat, Ahmed
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.pp961-973

Abstract

Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.
Photoplethysmogram signal reconstruction through integrated compression sensing and basis function aware shallow learning Muniraju, Usha; kumaran, Thangamuthu Senthil
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.pp1063-1076

Abstract

The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
An improved dynamic-layered classification of retinal diseases Nagamani, Gilakara Muni; Sudhakar, Theertagiri
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.pp417-429

Abstract

Retina is main part of the human eye and every disease shows the effect on retina. Eye diseases such as choroidal neovascularization (CNV), DRUSEN, diabetic macular edema (DME) are the main retinal diseases that damage the retina and if these damages are identified in the later stages, it is very difficult to reverse the vision for these retinal diseases. Optical coherence tomography (OCT) is a non-nosy image testing for finding the retinal diseases. OCT mainly collects the cross-section images of retina. Deep learning (DL) is used to analyze the patterns in several complex research applications especially in the disease prediction. In DL, multiple layers give the accurate detection of abnormalities in the retinal images. In this paper, an improved dynamic-layered classification (IDLC) is introduced to classify retinal diseases based on their abnormality. Image filters are used to filter the noise present in the input images. ResNet is the pre-trained model which is used to train the features of retinal diseases. Convolutional neural networks (CNN) are the DL model used to analyze the OCT image. The dataset consists of three types of OCT disease datasets from Kaggle. Evaluation results show the performance of IDLC compared with state-of-art algorithms. A better performance is obtained by using the IDLC and achieved the better accuracy. 
Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources Trong, Hien Chiem; Nguyen, Thuan Thanh; Nguyen, Thang Trung
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.pp74-81

Abstract

This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. 
A method for missing values imputation of machine learning datasets Hanyf, Youssef; Silkan, Hassan
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.pp888-898

Abstract

In machine learning applications, handling missing data is often required in the pre-processing phase of datasets to train and test models. The class center missing value imputation (CCMVI) is among the best imputation literature methods in terms of prediction accuracy and computing cost. The main drawback of this method is that it is inadequate for test datasets as long as it uses class centers to impute incomplete instances because their classes should be assumed as unknown in real-world classification situations. This work aims to extend the CCMVI method to handle missing values of test datasets. To this end, we propose three techniques: the first technique combines the CCMVI with other literature methods, the second technique imputes incomplete test instances based on their nearest class center, and the third technique uses the mean of centers of classes. The comparison of classification accuracies shows that the second and third proposed techniques ensure accuracy close to that of the combination of CCMVI with literature imputation methods, namely k-nearest neighbors (KNN) and mean methods. Moreover, they significantly decrease the time and memory space required for imputing test datasets.
A study on attention-based deep learning architecture model for image captioning Fudholi, Dhomas Hatta; Al-Faruq, Umar Abdul Aziz; Nayoan, Royan Abida N.; Zahra, Annisa
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.pp23-34

Abstract

Image captioning has been widely studied due to its ability in a visual scene understanding. Automatic visual scene understanding is useful for remote monitoring system and visually impaired people. Attention-based models, including transformer, are the current state-of-the-art architectures used in developing image captioning model. This study examines the works in the development of image captioning model, especially models that are developed based on attention mechanism. The architecture, the dataset, and the evaluation metrics analysis are done to the collected works. A general flow of image captioning model development is also presented. The literature search process carried out on Google Scholar. There are 36 literatures used in this study, including a specific image captioning development in Indonesian. It is done to take one point of view of image captioning development in a low resource language. Studies using transformer model generally achieves higher evaluation metric scores. In our finding, the highest evaluation scores on the consensus-based image description evaluation (CIDEr) c5 and c40 metrics are 138.5 and 140.5 respectively. This study gives a baseline on future development of image captioning model and brings the general concept of the image captioning development process including a picture of the development in low resource language.
Potential directions on coronary artery disease prediction using machine learning algorithms: A survey Vijayaraj, Anu Ragavi; Pasupathi, Subbulakshmi
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.pp658-672

Abstract

Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML-based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.
An approach for explaining group recommendations based on negotiation information Villavicencio, Christian; Schiaffino, Silvia; D´ıaz-Pace,, Jorge Andrés; Monteserin, Ariel
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.pp162-173

Abstract

Explaining group recommendations has gained importance over the last years. Although the topic of recommendation explanation has received attention in the context of single-user recommendations, only a few group recommender systems (GRS) currently provide explanations for their group recommendations. However, those GRS that support explanations, provide either explanations being highly reliant on the aggregation technique used for generating the recommendation (most of them trying to tackle shortcomings of the underlying technique), or explanations with a rich content but requiring users to provide considerable additional data. In this article, we present a novel approach for providing explanations of group recommendations, which are generated by a GRS based on multi-agent negotiation techniques. An evaluation of our approach with a user study in the movies domain has shown promising results. Explanations provided by our GRS system helped users during the decision-making process, since they modified the feedback given to recommended items. This is an improvement with respect to systems that do not provide explanations for their recommendations.

Page 6 of 12 | Total Record : 120

 4   5   6   7   8 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 15, No 1: February 2026 Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue