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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.
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Articles 50 Documents
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Search results for , issue "Vol 12, No 2: June 2023" : 50 Documents clear
Thai Hom Mali rice grading using machine learning and deep learning approaches Akara Thammastitkul; Jitsanga Petsuwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp815-822

Abstract

Thai Jasmine rice or Thai Hom Mali rice is a well-known rice type that originated in Thailand. Rice grain qualities are important in determining market pricing and are used in grading systems. The purpose of this research is to use machine learning and deep learning to improve the grading of Thai Hom Mali rice following standardized grading criteria. The appearance of grains and foreign items will determine the grade of rice. The experiment has two parts: grain categorization and rice grading. Multi-class support vector machine (SVM) and convolutional neural network (CNN) are proposed. There are 15 features used as input for multi-class SVM, including morphology and color features. With ImageNet pre-trained weights, CNN with DenseNet201 architecture is implemented. The experiment also tested into how CNN worked with both original and preprocessed images. The results are then compared to a neural network (NN) baseline approach. The CNN approach, which identified each rice variety using preprocessed images, archieved the greatest accuracy rate of 98.25%, with an average accuracy of 94.52% across six categories of rice grading.
An image-based convolutional neural network system for road defects detection Mohamed Anis Benallal; Mustapha Si Tayeb
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp577-584

Abstract

An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on applying YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91% of defect detection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device.
Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0 Khalif Amir Zakry; Irwandi Hipiny; Hamimah Ujir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp602-609

Abstract

Dancing is an art form of creative expression that is based on movement. Dancing comprises varying styles, pacing and composition to convey an artist’s expression. Thus, the classification of any dance to a certain genre or type depends on how accurate or similar it is to what is generally understood to be the specific movements of that dance type. This presents a problem for new dancers to assess if the dance movements that they have just learned is accurate or not to what the original dance type is. This paper proposed that deep learning models can classify dance videos of amateur dancers according to the similar movements of actions of several dance classes. For this study, AlexNet, ResNet and SqueezeNet models was used to perform training on multiple frames of actions of several dance videos for label prediction and the classification accuracy of the models during each training epoch is compared. This study observed that the average classification accuracy of the deep learning models is 94.9669% and is comparable to other approaches used for dance classifications.
A review of factors that impact the design of a glove based wearable devices Soly Mathew Biju; Obada Al Khatib; Hashir Zahid Sheikh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp522-531

Abstract

Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.
Design and implementation of the web (extract, transform, load) process in data warehouse application Seddiq Q. Abd Al-Rahman; Ekram H. Hasan; Ali Makki Sagheer
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp765-775

Abstract

Recently, due to an increase size and complexity of data in addition to the management issues, data storage is required a great deal of attention to employ it in realistic applications, so the requirement for designing and implementing a data warehouse system has become an urgent necessity, ETL represents a vital side of data warehouse architecture. This paper, designed data warehouse application contains a web ETL process that divides its work between the input device and server. This system proposed to solve the problem lack of partition of the work between the input device and server. The proposed and designed system can be used and applied in many branches and disciplines for its high performance in adding data, analyzing data using a web server and building queries. The analysis of the results has been proven that a faster time is achieved in cleaning data and transferring it between the remote parts of the system connected to the Internet. Where ETL without Missing for any data took 0.00582 seconds.
Combating propaganda texts using transfer learning Malak Abdullah; Dia Abujaber; Ahmed Al-Qarqaz; Rob Abbott; Mirsad Hadzikadic
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp956-965

Abstract

Recently, it has been observed that people are shifting away from traditional news media sources towards trusting social networks to gather news information. Social networks have become the primary news source, although the validity and reliability of the information provided are uncertain. Memes are crucial content types that are very popular among young people and play a vital role in social media. It spreads quickly and continues to spread rapidly among people in a peer-to-peer manner rather than a prescriptive. Unfortunately, promoters and propagandists have adopted memes to indirectly manipulate public opinion and influence their attitudes using psychological and rhetorical techniques. This type of content could lead to unpleasant consequences in communities. This paper introduces an ensemble model system that resolves one of the most recent natural language processing research topics; propaganda techniques detection in texts extracted from memes. The paper also explores state-of-the-art pretrained language models. The proposed model also uses different optimization techniques, such as data augmentation and model ensemble. It has been evaluated using a reference dataset from SemEval-2021 task 6. Our system outperforms the baseline and state-of-the-art results by achieving an F1-micro score of 0.604% on the test set.
An application of Vietnamese handwriting text recognition for information extraction from high school admission form Pham The Bao; Le Tran Anh Dang; Nguyen Duy Tam; Nguyen Nhat Truong; Pham Cung Le Thien Vu; Trinh Tan Dat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp568-576

Abstract

This paper presents an effective Vietnamese handwritten text recognition model by applying an improved convolutional recurrent neural networks (CRNNs) model to high school enrollment forms in Tay Ninh province, Vietnam. First, the proposed model extracts data areas containing text characters from forms. Then, we connect text boxes on the same row and divide the fields that containing text into three specific regions. Finally, we detect areas containing text characters for handwritten text recognition. We use word error rate (WER) to evaluate the recognition process and obtain a result of 0.3602. This result is one of the best solutions to the Vietnamese handwritten text recognition problem.
Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm Heba Mohammed Fadhil; Mohammed Abdullah; Mohammed Younis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp794-805

Abstract

In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. This study presents a hybrid greedy hill climbing algorithm (HGHC) that ensures both effectiveness and near-optimal results for generating a small number of test data. To make certain that the suggested HGHC outperforms the most used techniques in terms of test size. It is compared to others in order to determine its effectiveness. In contrast to recent practices utilized for the production of covering arrays (CAs) and mixed covering arrays (MCAs), this hybrid strategy is superior since allowing it to provide the utmost outcome while reducing the size and limit the loss of unique pairings in the CA/MCA generation.
New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM Omar Barki; Zouhair Guennoun; Adnane Addaim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp648-655

Abstract

In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.
Deep learning based object detection in nailfold capillary images Suma Kuncha Venkatapathiah; Sethu Selvi Selvan; Pranav Nanda; Manisha Shetty; Vikas Mallikarjuna Swamy; Kushagra Awasthi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp931-942

Abstract

Microcirculation in a subject can be examined and pathological changes can be assessed by utilizing capillaroscopy, which is a very safe, convenient and non-invasive approach. Using a microscope, doctors view the capillaries by looking through nailfold epidermis. Nailfold anatomy is ideal to evaluate the microcirculation and detect various diseases caused by vascular damages. Rheumatologists evaluate systemic diseases which involve damage in vasculature, by analyzing the red blood cells within the capillaries. Sometimes, capillary morphology may be useful as an early indicator while, severity of damage in capillary architecture may indicate internal organ involvement. Thus, in a capillaroscopic assessment, the doctor examines modifications in morphological and functional aspects of capillaries. These comprise of capillary diameter, visibility, distribution, length, microhemorrhages, blood flow and density. In this paper, a novel object detection algorithm is proposed based on deep learning architectures for detecting and locating various capillary loops in the nailfold region. Various characteristic features are extracted from the capillaries through image processing algorithms and in turn an attempt is made to differentiate between images of diseased subjects and healthy controls.

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