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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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Improving RepVGG model with variational data imputation in COVID-19 classification Kien Trang; An Hoang Nguyen; Long TonThat; Bao Quoc Vuong
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

Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.
Detection of traffic congestion based on twitter using convolutional neural network model Rifqi Ramadhani Almassar; Abba Suganda Girsang
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

Microblogging is a form of communication between users to socialize by describing the state of events in real-time. Twitter is a platform for microblogging. Indonesia is one of the countries with the largest Twitter users, people can share information about traffic jams. This research aims to detect traffic jams by extracting tweets in the form of vectors and then inserting them into the Convolution neural network (CNN) model and getting the best model from CNN+Word2Vec, CNN+FastText, and support vector machine (SVM). Data retrieval was conducted using the Rapidminer application. Then, the context of the tweets was checked so that there were 2777 data consisting of 1426 congestion road data and 1351 smooth road data. The data was taken from certain coordinate points in around Jakarta, Indonesia. Then, preprocessing and changes to vector form were carried out using the Word2Vec and FastText methods, then inserted into the CNN model. The results of CNN+Word2Vec and CNN+FastText were compared to the SVM method. The evaluation was done manually using the actual traffic conditions. The highest result obtained using test data by the CNN+FastText method are 86.33% while CNN+Word2Vec is 85.79% and SVM is 67.62%.
Classification of customer feedbacks using sentiment analysis towards mobile banking applications Nurazzah Abd Rahman; Seri Dahlia Idrus; Noor Latiffah Adam
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

Innovation and technology have subsequently transformed banking industry’s way of delivering products and services to their customer. Mobile banking is an effective way of performing transaction as it can be performed anywhere and anytime. The evolution of banking experience is important to fulfil customers’ need and demand especially in highly competitive banking industry. Through mobile banking application, customer can express their satisfaction and dissatisfaction directly on the application store platform. The fulfilment of customer’s satisfaction is important to avoid customer attrition. This research focused on customer feedbacks towards six mobile banking application in Malaysia which is Maybank, Commerce International Merchant Bankers (CIMB), Public Bank, Hong Leong Bank, Rashid Hussein Bank (RHB) and AmBank. This research aims to identify keywords related to customer feedback towards mobile banking, classify the sentiment and evaluate the accuracy performance by using supervised machine learning algorithm of support vector machine (SVM) and Naïve Bayes (NB). The result shows that linear SVM is the best model with the highest value in all accuracy, precision, recall, including F1-score with value 97.17%, 97.21%, 97.17% and 97.18% respectively. With this high accuracy value, this model would have better performance in analyzing the classification of customer feedback in mobile banking application.
Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm Ridha Sefina Samosir; Edi Abdurachman; Ford Lumban Gaol; Boy Subirosa Sabarguna
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

Image segmentation is often faced by low contrast, bad boundaries, and inhomogeneity that made it difficult to separate normal and abnormal tissue. Therefore, it takes long periodto read and diagnose brain tumor patients. The aim of this study was to applied hybrid methods to optimize segmentation process of magnetic resonance image of brain. In this study, we divide the brain tumor images with double density dual-tree complex wavelet transform (DDDTCWT), continued by convolutional neural network (CNN), and optimized by genetic algorithm (GA) with 48 combinations yielding excellent results. The F-1 score was 99.42%, with 913 images test data. The training images consist of 1397 normal MRI images and 302 tumor magnetic resonance imaging (MRI) images resized by 32 x32 pixels. The DDDTCWT transforms the input images into more detail than ordinary wavelet transforms, and the CNNs will recognize the pattern of the output images. Additionally, we applied the GA to optimize the weights and biases from the first layer of the CNNs layers. The parameters used for evaluating were dice similarity coefficient (DSC), positive present value (PPV), sensitivity, and accuracy. The result showed that the combination of DDDTCWT, CNN, and GA could be used to brain MRI images and it generated parameters value more that 95%.
Proposing a route recommendation algorithm for vehicles based on receiving video Phat Nguyen Huu; Phuong Tong Thi Quynh; Thien Pham Ngoc; Quang Tran Minh
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

In this paper, we propose a method to classify traffic status for the route recommendation system based on received videos. The system will determine the number of vehicles in the region of interest (ROI) to determine and calculate the coefficient of variation (CV) based on the videos extracted from cameras at intersections. It then predicts the congested traffic junctions in the city. The data then goes through the routing module and is transmitted to the website to find the best path between the source and destination requested by users. In this system, we use you only look once (YOLOv5) for vehicle detection and the A* algorithm for routing. The results show that the proposed system achieves 91.67% accuracy in detecting traffic status comparing with YOLOv1, deep convolutional neural network (DCNN), convolutional neural network (CNN), and support vector machine (SVM) models as 91.2%, 90.2%, 89.5%, and 85.0%, respectively. 
A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms Vigneshwaran Pandi; Prasath Nithiyanandam; Sindhuja Manickavasagam; Islabudeen Mohamed Meerasha; Ragaventhiran Jaganathan; Muthu Kumar Balasubramanian
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.pp1085-1093

Abstract

An exponential growth posting on the web about the product reviews on social media, there has been a great deal of examination being done on sorting out the purchasing behaviors of the client. This paper depends on utilizing twitter for sentiment analysis to comprehend the customer purchasing behavior. There has been a significant increase in e-commerce, particularly in persons purchasing products on the internet. As a result, it becomes a fertile hotspot for opinion analysis and belief mining. In this investigation, we look at the problem of recognizing and anticipating a client's purchase goal for an item. The sentiment analysis helps to arrive at a more indisputable outcome. In this study, the support vector machine, naive Bayes, and logistic regression methods are investigated for understanding the customer's sentiment or opinion on a specific product. These strategies have been demonstrated to be genuinely for making predictions using the analysis models which examine the client's conclusion/sentiment the most precisely. The exactness for each machine learning algorithm will be analyzed and the calculation which is the most precise would be viewed as ideal.
Selecting goldfish broods use the weighted product and simple additive weighting methods Ramadiani Ramadiani; Surya Adithama; Muhammad Labib Jundillah
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

Majalaya carp is a freshwater fish that has important economic value and is widespread in Indonesia. Goldfish is the most cultivated fish because it has many advantages both physiologically and genetically. Several factors of assessment in the selection of superior brood stock that can be considered in the cultivation of goldfish cultivators are; ideal body weight, fish movement, physical deformities, scales, and the base of the tail. All of these factors can help in the decision-making process for superior goldfish. This study uses two methods, namely the simple additive weighting (SAW) method and the weighted product (WP) method. Based on the results of research that has been carried out on 20 superior broodfish of Majalaya goldfish, the level of accuracy is obtained by comparing with existing data. The WP method gets an accuracy value of 90% while the SAW method gets an accuracy value of 80%.
On searching the best mode for forex forecasting: bidirectional long short-term memory default mode is not enough Seng Hansun; Farica Perdana Putri; Abdul Q. M. Khaliq; Hugeng Hugeng
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

Presently, the Forex market has become the world’s largest financial market with more than US$5 trillion daily volume. Therefore, it attracts many researchers to learn its traded currency pairs characteristics and predict their future values. Here, we propose simple three layers Bidirectional long short-term memory (Bi-LSTM) networks for Forex forecasting with four different merge modes. Moreover, the proposed model is also compared to the conventional long short-term memory (LSTM) networks with the same architecture. Five major Forex currency pairs, namely AUD/USD, EUR/USD, GBP/USD, USD/CHF, and USD/JPY, with more than ten years of historical records are considered in this study. It is revealed from the experimental results that among four available merge modes, the concatenation mode as the default merge mode in Bi-LSTM networks is actually the less preferred mode for Forex forecasting (Root mean square error 0.30685517, mean absolute error 0.27442235, mean absolute percentage error 0.827108%). Moreover, Bi-LSTM average mode gets the highest  score that could achieve 89.579%. Therefore, the proposed three layers Bi-LSTM networks could provide a baseline result for developing a good trading strategy in Forex forecasting.
Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding Dheyaa Jasim Kadhim; Muna Hadi Saleh; Sadiq Jassim Abou-Loukh
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.pp984-994

Abstract

The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. 
Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme Hatem Fahd Al-Selwi; Nawaid Hassan; Hadhrami Bin Ab Ghani; Nur Asyiqin binti Amir Hamzah; Azlan Bin Abd. Aziz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1169-1177

Abstract

Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

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