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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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CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait Muhammad Noor Fakhruzzaman; Sie Wildan Gunawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internet-connected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often cluttered with clickbait news. Leveraging state-of-the-art bidirectional encoder representation from transformers (BERT), a lightweight web application is developed. This study offloaded the computing resources needed to train the model on a separate instance of virtual server and then deployed the trained model on the cloud, while the client-side application only needs to send a request to the API and the cloud server will handle the rest, often known as three-layer architecture. This study designed and developed a web-based application to detect clickbait in Indonesian using IndoBERT as its language model. The application usage and potentials were discussed. The source code and running application are available for public with a performance of mean receiver operating characteristic-area under the curve (ROC-AUC) of 89%.
Hardware sales forecasting using clustering and machine learning approach Rani Puspita; Lili Ayu Wulandhari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1074-1084

Abstract

This research is a case study of an information technology (IT) solution company. There is a problem that is quite crucial in the hardware sales strategy which makes it difficult for the company to predict the number of various items that will be sold and also causes the excess or shortage in hardware stocking. This research focuses on clustering to group various of items and forecast the number of items in each cluster using a machine learning approach. The methods used in clustering are k-means clustering, agglomerative hierarchical clustering (AHC), and gaussian mixture models (GMM), and the methods used in forecasting are autoregressive integrated moving average (ARIMA) and recurrent neural network-long short-term memory (RNN-LSTM). For clustering, k-means uses two attributes, namely "Quantity and Stock" as the best feature in this case study. Using these features the k-means obtain silhouette results of 0.91 and davies bouldin index (DBI) values of 0.34 consisting of 3 clusters. While for forecasting, RNN-LSTM is the best method, where it produces more cost savings than the ARIMA method. The percentage of the difference in saving costs between ARIMA and RNN-LSTM to the actual cost is 83%.
FrBMedQA: The first French biomedical question answering dataset Zakaria Kaddari; Toumi Bouchentouf
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

FrBMedQA is the first French biomedical question answering dataset, containing 41k+ passage-question instances. It was automatically constructed in a cloze-style manner, from biomedical French Wikipedia articles. To test the validity and difficulty of the dataset, we experimented with four statistical baseline models, a biomedical bidirectional encoder representations from transformers (BERT)-based model, and two French BERT-based language model. We also did human evaluation on a subset of the test set. All the three tested models were not able to surpass the best performing baseline model. Human performance at 61.11% is leading the leaderboard with more than 8% from the best performing model. We made available the dataset and the code to reproduce our results.
New implementation of the abstract design for non-iterative hash functions with 1024 digest length NIHF-1024 Abouchouar Abdallah; Fouzia Omary
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The non-iterative hash function design is a domain extension that uses a novel concept to fill a gap in existing cryptographic designs. This design avoids the structural issues that plagued prior paradigms, particularly those applying the Merkel-Damgârd design, by presenting a novel approach. Non-iterative hash function design, on the other hand, is an abstract idea that necessitates the specification of the internal transformation in order to test and experiment it. To achieve this goal, this article describes the algorithm that implements the internal transformation based on the main characteristics of the new model. The avalanche effect's results are provided in the experiment section, as well as a preliminary validation stage of the national institute of standards and technology (NIST's) cryptographic algorithm validation program (CAVP), which employs a set of test vectors to verify algorithm accuracy and implementation errors.
Topographical distribution of the human brain to Plukenetia volubilis-based omega-3,6,9 enriched egg consumption Phakkharawat Sittiprapaporn; Prayoon Suyajai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Docosahexaenoic acid (DHA) is related to cognitive development. Consumers' brain health could benefit from changes in the omega 6:3 ratio. This study aimed to examine how the Plukenetia volubilis-based omega-3,6,9 enriched egg supplementation affects electrical brain activity during the attention/inhibition (Go/NoGo) test. Healthy subjects (n = 20) were chosen at random to eat 2 boiled Plukenetia volubilis-based omega-3,6,9 enriched eggs for 12 weeks. The ePrime v.3.0 application recorded behavioral performance during the Go/NoGo test during the electroencephalographic recording. The finding of this study was that twelve weeks of boiled Plukenetia volubilis-based omega-3,6,9 enriched egg consumption significantly decreased the reaction time responses compared to the baseline. The topographical distribution revealed that the mean amplitude of N1 produced a slightly larger amplitude in the 12th week compared to the baseline. The P3 component, following N1, was also larger in the 12th week compared to the baseline. After 12 weeks of consuming the Plukenetia volubilis-based omega-3,6,9 enriched eggs, the central nervous system activities during a Go/NoGo test were believed to be enhanced. 
Machine learning model for green building design prediction Mustika Sari; Mohammed Ali Berawi; Teuku Yuri Zagloel; Rizka Wulan Triadji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Green Building (GB) is a design concept that implements sustainable processes and green technologies in the building’s life cycle. However, the design process of GB tends to take longer than conventional buildings due to the integration of various green requirements and performances into the building design. Technological advances are continually improving the quality of human life by providing solutions to problems they encounter, such as the machine learning (ML) technique utilized to develop predictive and classification models. This study aims to develop a GB design prediction by employing an ML approach by considering four GB design criteria: energy efficiency, indoor environmental quality, water efficiency, and site planning. A dataset of GB projects collected from a private construction company based in Jakarta was used to train and test the ML model. Mean Square Error (MSE) was used to evaluate the model accuracy. The comparison of MSE results of the conducted experiments showed that the combination of the ANN method with the IF-ELSE algorithm resulted in the most accurate ML model for GB design prediction with an MSE of 1.3, creating a predictive model that improves the time efficiency of GB design process.
Simulation of pedestrian movements using a fine grid cellular automata model Siamak Sarmady; Fazilah Haron; Abdullah Zawawi Talib
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Crowd simulation is used in evacuation and crowd safety inspections and in the study of the performance of crowd systems and animations. Cellular automata are extensively utilized in crowd modeling. In regular cellular automata models, each pedestrian occupies a single cell with the size of a pedestrian body. The movements of pedestrians resemble those of chess pieces on a chessboard because the space is divided into relatively large cells. Furthermore, all pedestrians feature the same body size and speed. This study proposes a fine grid cellular automata model that uses small cells and allows pedestrian bodies to occupy several cells. The model allows the use of different body sizes, shapes, and speeds for pedestrians. The model is also used to simulate the movements of pedestrians toward a specific target. A typical walkway scenario is considered to test and evaluate the proposed model. Pedestrian movements are smooth because of the fine grain discretization of movements, and simulation results match the empirical speed–density graphs with good accuracy.
Machine learning based chroma phase offset detection and correction in motion video Advait Mogre; Shekhar Madnani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Generally, chroma phase or hue offset issues within a scene are hard to detect, without a reference or context (i.e. some apriori knowledge about how certain objects within the scene should actually appear in terms of their hue). Moreover, when it comes to skin/flesh tones, hue deviation can be noticeable and can markedly degrade the viewer quality of experience(QoE), whenever it does occur. However a lot of research has gone into flesh tone detection, specifically, the color gamut within which flesh tone is present. This topic has been well documented in the literature with respect to various color spaces: red, green, blue (RGB) and YIQ. Therefore, overall issues with chroma offset or hue within the video content could potentially be approached by extracting and analyzing a reliable reference, such as skin or flesh tone (if present), within some allowable deviation. This involves machine learning (ML) based facial recognition and tracking followed by skin tone region recognition within the detected facial sequence (i.e. Region of Interest). The skin region serves as a ‘self-reference’ in order to discern any inherent phase offset within the content. Finally, the angular chroma deviation discerned can then be used for subsequent correction as well. 
Classification of nutrient deficiency in oil palms from leaf images using convolutional neural network Muhammad Ikmal Hafiz Razali; Muhammad Asraf Hairuddin; Aisyah Hartini Jahidin; Mohd Hanafi Mat Som; Megat Syahirul Amin Megat Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Oil palm is a perennial plant that thrives well in tropical climate. Apart from humid environment, the plant also requires a wide variety of nutrients. Any deficiencies will directly affect its growth and palm oil production. These can often be detected from the change of leaf colour and texture. Deviations from the standard dark green colour indicates lack of certain nutrients. Therefore, this study proposes convolutional neural network (CNN) to classify nutrient deficiency in oil palms using leaf images. A total of 180 leaf images are acquired using standardized protocol. The samples are evenly distributed into healthy, nitrogen-deficient, and potassium-deficient groups. Residual network (ResNet)-50, visual geometry group-16 (VGG16), Densely connected network (DenseNet)-201, and AlexNet are trained and tested using the randomized samples. Each attained classification accuracies of 96.7%, 100%, 98.3%, and 100% respectively. Despite yielding similar performance, AlexNet is the more computational efficient architecture with less convolutional layers compared to VGG-16.
Automated dynamic schema generation using knowledge graph Priyank Kumar Singh; Sami Ur Rehman; Darshan J; Shobha G; Deepamala N
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

On the internet where the number of database developers is increasing with the availability of huge data to be stored and queried. Establishing relations between various schemas and helping the developers by filtering, prioritizing, and suggesting relevant schema is a requirement. Recommendation system plays an important role in searching through a large volume of dynamically generated schemas to provide database developers with personalized schemas and services. Although many methods are already available to solve problems using machine learning, they require more time and data to learn. These problems can be solved using knowledge graphs (KG). This paper investigates building knowledge graphs to recommend schemas. 

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