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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 55 Documents
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Search results for , issue "Vol 12, No 4: December 2023" : 55 Documents clear
Analysis of the evolution of advanced transformer-based language models: experiments on opinion mining Nour Eddine Zekaoui; Siham Yousfi; Maryem Rhanoui; Mounia Mikram
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1995-2010

Abstract

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
Valve control system on a venturi to control FiO2 a portable ventilator with fuzzy logic method based on microcontroller Hadi Pranoto; Arief Marwanto; Suryani Alifah; Lukman Abdul Fatah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1593-1602

Abstract

The results of several studies for portable ventilators state that it is difficult toregulate oxygen levels of fraction of inspired oxygen (FiO2) that are inaccordance with what is needed directly during the implementation of oxygentherapy. Some portable ventilators cannot set the FiO2, so the ventilators workwith a fixed FiO2. To overcome this problem, medical oxygen with a level of100% is lowered by mixing with free air with an oxygen content of about 40%.Mixing is carried out using a venturi with a large/wide hole that can beadjusted using a slated disk rotated by a direct current motor. The rotationcontrol method uses Mamdani's fuzzy logic. The results of clinically lab-scaletesting show that the fuzzy logic control system is able to control the averageerror pressure of 10.3%, better when compared to the on-off control method,which is 14.5%. The fuzzy logic method is able to increase the accuracy ofFiO2 on a portable ventilator.
Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5 Hamzah Abdulmalek Al-Haimi; Zamani Md Sani; Tarmizi Ahmad Izzudin; Hadhrami Abdul Ghani; Azizul Azizan; Samsul Ariffin Abdul Karim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1585-1592

Abstract

This project aims to develop a vision system that can detect traffic lightcounter and to recognise the numbers shown on it. The system used you onlylook once version 3 (YOLOv3) algorithm because of its robust performanceand reliability and able to be implemented in Nvidia Jetson nano kit. A totalof 2204 images consisting of numbers from 0-9 green and 0-9 red. Another80% (1764) from the images are used for training and 20% (440) are used fortesting. The results obtained from the training demonstrated Totalprecision=89%, Recall=99.2%, F1 score=70%, intersection over union(IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2%and the estimate total confidence rate for red and green are 98.4% and 99.3%respectively. The results were compared with the previous YOLOv5algorithm, and the results are substantially close to each other as the YOLOv5accuracy and recall at 97.5% and 97.5% respectively.
User identification based on short text using recurrent deep learning Huda Hallawi; Huda Ragheb Kadhim; Zahraa Najm Abdullah; Noor D. AL-Shakarchy; Dhamyaa A. Nasrawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1812-1820

Abstract

Technological development is a revolutionary process by this time, it ismainly depending on electronic applications in our daily routines like(business management, banking, financial transfers, health, and other essentialtraits of life). Identification or approving identity is one of the complicatedissues within online electronic applications. Person’s writing style can beemployed as an identifying biological characteristic in order to recognize theidentity. This paper presents a new way for identifying a person in a socialmedia group using comments and based on the Deep Neural Network. Thetext samples are short text comments collected from Telegram group in Arabiclanguage (Iraqi dialect). The proposed model is able to extract the person'swriting style features in group comments based on pre-saved dataset. Theanalysis of this information and features forms the identification decision.This model exhibits a range of prolific and favorable results, the accuracy thatcomes with the proposed system reach to 92.88% (+/- 0.16%).
A novel modified dandelion optimizer with application in power system stabilizer Widi Aribowo; Bambang Suprianto; Aditya Prapanca
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2033-2041

Abstract

This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA) and the original dandelion optimizer (DO). The simulation results show that the proposed method, which is a modified dandelion optimizer, provides promising performance.
Automatic brain tumor detection using adaptive region growing with thresholding methods Kadry Ali Ezzat; Lamia Nabil Omran; Ahmed Adel Ismail; Ahmed Ibrahim Bahgat El Seddawy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1569-1576

Abstract

Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Modelling mechanisms for measurable and detection based on artificial intelligence Raghad Abdul Hadi Abdul Qader; Marwa Jassim Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp2042-2047

Abstract

One of the trendiest areas in the field of materials science is Artificial Intelligence (AI) based physical applications. Typically, more time and resources are needed for traditional experiments and statistical methods. Thus, there is a growing need for applications of AI in the simulation and investigation of novel materials. Usually, there are significant restrictions because there are not any benchmark datasets, sophisticated pre-processing mechanisms, prediction modelling mechanisms, or simulation tools in the literature on materials. This work aims to attempt for examining computational and experimental data-based AI processes. In addition, the state of research into developing new materials and utilizing AI in material modelling tools is implemented. As long as, AI can be used in materials to improve efficiency and prediction accuracy. Also, it is very difficult to determine great learning models, involving data preparation, model architecture, data management, and simulation techniques. Finally, it has been discussed the challenges in realizing AI-based applications in the field of materials science.
Developing Viola Jones' algorithm for detecting and tracking a human face in video file Ansam Nazar Younis; Fawziya Mahmood Ramo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1603-1610

Abstract

Face detecting and tracking in video clips is very important in many areas ofdaily life. All institutions, public departments, streets, and large stores usecameras from a security point of view, and detecting and tracking humanfaces is necessary for indexing and preserving information concerning thevisual media. This paper presents a novel method for hybridizing theViola_Jones face detection algorithm to track and identify a human face invideo sequences. The method represents a combination of Viola Jones'algorithm with a measured normalized cross-correlation (NCC) algorithmwith a template matching method using the Manhattan distance measureequation in the video between successive sequences After that, the fuzzylogic method is added in comparing the image of the face to be detected withthe images of templates taken in the proposed algorithm, which increased theaccuracy of the results, which reached 99.3%.
A novel meta-embedding technique for drug reviews sentiment analysis Aye Hninn Khine; Wiphada Wettayaprasit; Jarunee Duangsuwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1938-1946

Abstract

Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.
Productivity of incident management with conversational bots-a review Orlando Iparraguirre-Villanueva; Luz Obregon-Palomino; Wilson Pujay-Iglesias; Fernando Sierra-Liñan; Michael Cabanillas-Carbonell
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1543-1556

Abstract

The use of conversational agents (bots) in information systems managed by company’s increases productivity in the development of activities focused on processes such as customer service, healthcare, and presentation. The present work is a systematic literature review that collects articles from 2019 to 2022 in the databases Scopus, Springer, Willey, Indexes-Csic, Taylor & Francis, Pubmed, and Ebsco Host. PRISMA methodology was used to systematize 47 relevant articles. As a result of the analysis, 2/19 very important benefits were obtained, which are: helping to obtain information and facilitating customer service; as for the types of conversational bots, a total of 9 types were found, of which conversational agents and chatbots with artificial intelligence (AI) are the most common; in the case of processes, 3/5 processes that optimize conversational bots were found, where the most prominent are: teaching process, health processes, and customer service processes. An architecture model for conversational bots in incident management is also proposed.

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